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+<!DOCTYPE HTML>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html;
+ charset=utf-8">
+
+ <link rel="stylesheet" type="text/css" href="style/style.css">
+</head>
+
+<body>
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li><a href="index.html">Accueil</a></li>
+ <li><a href="membres.html">Membres</a></li>
+ <li><a href="manif.html">Manifestations</a></li>
+ <li><a href="projets.html">Projets</a></li>
+ <li class="selected"><a href="contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+
+ <h1>Nous contacter</h1>
+
+ <p align="justify"> C'est très simple.<br> Il suffit d'envoyer
+ un mail aux adresses suivantes en indiquant "[bioss]" au début du
+ sujet du message et en veillant à remplacer "_$à$_" par "@" :<br>
+ <ul>
+ <li> Anne Siegel : anne.siegel_$à$_irisa.fr
+ <li> Cédric Lhoussaine : cedric.lhoussaine_$à$_univ-lille1.fr
+ <li> Élisabeth Remy : elisabeth.remy_$à$_univ-amu.fr
+ <li> Gregory Batt : gregory.batt$à$inria.fr
+ </ul>
+ ou plus simplement en utilisant l'adresse générique :
+ <!-- gt-bioss-request_$à$_listes.math.cnrs.fr. -->
+ contact_$à$_bioss-cnrs.fr<br><br></p>
+
+ <h1>S'inscrire à la liste de diffusion du groupe</h1>
+
+ <p align="justify"> Si vous désirez recevoir les informations relatives
+ au groupe, vous pouvez vous inscrire à notre liste de diffusion depuis
+ l'adresse :<br>
+ https://listes.mathrice.fr/math.cnrs.fr/subscribe/gt-bioss<br><br>
+ Si vous rencontrez des problèmes, n'hésitez pas nous contacter.</p>
+ </div>
+ <!-- <div id="banner"></div> -->
+ </div>
+
+ <div id="footer">
+ <p><a href="index.html">Accueil</a>
+ | <a href="membres.html">Membres</a>
+ | <a href="manif.html">Manifestations</a>
+ | <a href="projets.html">Projets</a>
+ | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+</body></html>
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+++ b/index.html
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+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8">
+
+ <link rel="stylesheet" type="text/css" href="style/style.css">
+</head>
+
+<body>
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li class="selected"><a href="index.html">Accueil</a></li>
+ <li><a href="membres.html">Membres</a></li>
+ <li><a href="manif.html">Manifestations</a></li>
+ <li><a href="projets.html">Projets</a></li>
+ <li><a href="contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <!-- insert your sidebar items here -->
+ <h3>News</h3>
+
+<h4><a href="manif/covid-2020/sem_virt.html">Séminaire virtuel</a> et forum d'échange du GT Bioss</h4>
+<h5>Vendredi à 13h à partir du 2 octobre 2020</h5>
+<p>Pour nous aider à l'organiser, répondez à notre <a href="">sondage</a>!</p>
+
+<h4>Journée annuelle du GT Bioss</h4>
+<h5>7 novembre 2019</h5>
+<p>La 5ème édition des journées annuelles du GT Bioss se déroulera juste après la <a href="http://www.gdr-bim.cnrs.fr/?page_id=160">journée nationale du GDR BiM</a> à l'Université Denis Diderot.
+<br><a href="manif/jnbioss_201911/jnbioss_201911.html">+ d'infos</a><br>
+</p>
+
+<h4><br>
+</h4>
+<h4>Session thématique Bioss à JOBIM 2019</h4>
+<h5>3 juillet (après-midi) 2019</h5>
+<p>Bioss et le <a href="https://biosynsys2015.sciencesconf.org/">GDR BioSynSys</a> organisent une session thématique "Predictive approaches for biological systems engineering" dans le cadre de JOBIM 2019.<br>
+ <a href="https://jobim2019.sciencesconf.org/resource/page/id/5">+ d'infos.</a></p>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Bienvenue sur le site du GT Bioss</h1>
+
+ <h4>Présentation synthétique</h4>
+ <p align="justify">
+<!--StartFragment-->
+
+</p>
+<p class="MsoNormal" style="text-align: justify;"><span style="">Le groupe Bioss est un groupe de travail scientifique, soutenu par le CNRS
+au travers des Groupes de recherche Informatique mathématique (GDR IM) et
+Bio-informatique moléculaire (GDR BiM), rassemblant la communauté des chercheurs
+et enseignants-chercheurs français autour de la modélisation des
+systèmes biologiques, thématique centrale de la biologie des systèmes à la
+frontière de </span><span style="mso-ansi-language:
+FR">l'informatique fondamentale, des</span><span style="mso-ansi-language:
+FR"> mathématiques discrètes (et plus généralement des mathématiques), avec la
+biologie moléculaire et la médecine.<o:p></o:p></span></p>
+
+
+<!--EndFragment-->
+
+
+ <h4>Biologie des systèmes</h4>
+ <p align="justify">
+
+<!--StartFragment-->
+
+</p>
+<p class="MsoNormal" style="text-align: justify;"><span style="">C'est un domaine de recherche dont l'objectif est de comprendre comment
+fonctionnent les systèmes biologiques dans leur ensemble, en intégrant
+différents niveaux d'informations, et à partir de l’étude des relations et des
+interactions entre les composants de ces systèmes. <br>
+</span></p>
+<p class="MsoNormal" style="text-align: justify;"><span style="">Ce domaine est par nature
+interdisciplinaire, puisque pour répondre à des questions biologiques, il
+traite de problèmes liés à l’observation de la réponse cellulaire (analyse de
+données), à l’intégration de données pour l’identification d’interactions
+(méthodes statistiques, apprentissage), à la modélisation formelle et numérique
+du comportement du système (modèles formels, modèles symboliques, modèles
+numériques), à l’étude de modèles de processus cellulaires (mathématiques
+appliquées, informatique théorique), allant jusqu’à leur contrôle (automatique,
+vérification de modèles). <br>
+ </span></p>
+<p class="MsoNormal" style="text-align: justify;"><span style="mso-ansi-language:
+FR">Deux aspects doivent être pris en compte.
+Premièrement ces systèmes sont difficiles à observer et manipuler
+expérimentalement.
+Deuxièmement de multiples échelles spatiales et/ou temporelles doivent
+être
+prises en compte tant pour les acteurs (gènes, protéines, métabolites,
+organites, cellules, populations) que pour leurs interactions
+(transcription,
+transformations chimiques, signalisation, diffusion, échanges,
+communications
+entre cellule, signaux provenant de l’environnement). Ces deux
+caractéristiques génèrent de nouvelles questions de nature informatique
+et mathématiques qui sont au coeur des thématiques du groupe de
+travail.<o:p></o:p></span></p>
+
+
+<!--EndFragment-->
+
+ <h4>Modélisation symbolique des systèmes biologiques</h4>
+ <p align="justify">
+<!--StartFragment-->
+
+</p>
+<p class="MsoNormal" style="text-align: justify;"><span style="mso-ansi-language:
+FR">Cette thématique vise à développer des méthodes informatique et
+mathématiques facilitant la modélisation, l’analyse et la compréhension
+des
+systèmes biologiques dynamiques "complexes. Ces développements
+méthodologiques particuliers, souvent utilisés en complément des
+méthodes de
+modélisation et d’analyse traditionnelles, sont motivés par le constat
+que les
+systèmes biologiques diffèrent des systèmes physiques par plusieurs
+aspects
+fondamentaux. En particulier, la modélisation, la spécification, le
+contrôle et
+la vérification de modèles qualitatifs, ainsi que l’étude de leurs
+invariants pour
+faire émerger des propriétés robustes y jouent un rôle central.<o:p></o:p></span></p>
+
+
+<p class="MsoNormal" style="text-align: justify;"><span style="mso-ansi-language:
+FR">En dehors d’une meilleure compréhension du fonctionnement des
+systèmes
+biologiques, des contributions plus théoriques sont également attendues.
+ Parmi
+les principales questions on peut citer : En quoi les systèmes
+biologiques
+sont-ils formalisables ? Quels nouveaux mécanismes de transmission
+d'information sont à l'oeuvre en biologie moléculaire et cellulaire
+? Mais
+également, qu'est-ce que l'informatique peut apporter à la biologie, au
+delà
+des approches de modélisation numérique ?<o:p></o:p></span></p>
+<!--EndFragment-->
+<h4><br>
+</h4>
+<h4>Mots-clés / contours</h4>
+<!--StartFragment-->
+
+<p class="MsoNormal" style="text-align: justify;"><span style="mso-ansi-language:
+FR">Modélisation de systèmes biologiques ; Systèmes dynamiques discrets, continus
+et hybrides ; Systèmes formels ; Langages de modélisation ; Sémantique (y
+compris stochastique) ; Vérification de modèles ; Réduction, prédiction (sous
+incertitude) ; Inférence d’interactions et de règles à partir de données
+biologiques.<o:p></o:p></span></p>
+
+
+<!--EndFragment-->
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
+ </div>
+
+ <div id="content_footer"></div>
+
+ <div id="footer">
+ <p><a href="index.html">Accueil</a>
+ | <a href="membres.html">Membres</a>
+ | <a href="manif.html">Manifestations</a>
+ | <a href="projets.html">Projets</a>
+ | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+
+
+</body></html>
diff --git a/manif.html b/manif.html
new file mode 100644
index 0000000000000000000000000000000000000000..a40d059e73089af2c840ecc9e6d44817303befb9
--- /dev/null
+++ b/manif.html
@@ -0,0 +1,365 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique"> <meta name="keywords" content="Modélisation symbolique de systèmes biologiques, Systèmes
+ dynamiques discrets, Systèmes formels, Langages de modélisation,
+ Sémantique, Vérification de modèles, Réduction, Prédiction sous
+ incertitude, Inférences d'interactions et de règles, Systèmes
+ hybrides"> <meta http-equiv="content-type" content="text/html; charset=UTF-8"> <link rel="stylesheet" type="text/css" href="style/style.css">
+
+ </head>
+
+<body>
+
+<div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html"> <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected page - to
+ highlight which page you're on -->
+ <li><a href="index.html">Accueil</a></li>
+ <li><a href="membres.html">Membres</a></li>
+ <li class="selected"><a href="manif.html">Manifestations</a></li>
+ <li><a href="projets.html">Projets</a></li>
+ <li><a href="contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+ <div id="site_content">
+ <!-- <div id="banner"></div> -->
+ <div id="sidebar_container">
+ <div class="sidebar"> </div>
+ <div class="sidebar">
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/"> <img src="img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/"> <img src="img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/"> <img src="img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ </div>
+ </div>
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Événements en lien avec le groupe</h1>
+ <p align="justify">L'animation de la communauté de biologie
+ systémique symbolique existe depuis une quinzaine d'années
+ au travers de différentes actions/événements. Ici, nous
+ dressons par ordre anti-chronologique la liste des
+ manifestations co-organisées par Bioss ainsi qu'une liste
+ non exhaustive des différentes actions selon leur nature
+ (conférences, écoles, journées thématiques) en lien avec les
+ activités de Bioss depuis septembre 2014.</p>
+ <h2>Manifestations de Bioss</h2>
+ <ul>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 4 juin 2021 à 13h.<br>
+ Intervenants: Sabine Pérès et Hérault (<a href="manif/covid-2020/sem_virt.html">résumés</a>)<br/>
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 7 mai 2021 à 13h.<br>
+ Intervenants: Matthias Fuegger et Danilo Dursoniah (<a href="manif/covid-2020/sem_virt.html">résumés</a>)<br/>
+ <i>L'exposé intialement prévu de Manish KUSHWAHA reporté à l'automne.</i>
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 2 avril 2021 à 13h.<br>
+ Intervenants: Francis Mairet et Antrea Pavlou (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 12 mars 2021 à 13h.<br>
+ Intervenants: Samuel Chaffron et Stéphanie Chevalier (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 5 février 2021 à 13h.<br>
+ Intervenants: Olivier Gandrillon et Aurélien Naldi (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 8 janvier 202<b>1</b> à 13h.<br>
+ Intervenants: Thomas E. Gorochowski et Olivier Borkowski (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ <li> <a href="https://easychair.org/cfp/SinCellMod2020">SinCellMod-2020: Single cell data in network modeling</a><br>
+ Dates : 1er et 2 décembre 2020<br>
+ Lieu : Virtuel.<br>
+ </li>
+ <li> <a href="manif/biossia_202011/biossia202011.html">Journée Bioss-IA 2020</a><br>
+ Dates : 24 novembre 2020<br>
+ Lieu : Virtuel.<br>
+ </li>
+ <li><b>Séminaire virtuel mensuel</b><br>
+ Dates: vendredi 6 novembre à 13h.<br>
+ Intervenantes: Déborah Boyenval et Loïc Paulevé (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ <li>Séminaire virtuel mensuel<br>
+ Dates: vendredi 2 octobre à 13h.<br>
+ Intervenantes: Caroline Baroukh et Anais Baudot (<a href="manif/covid-2020/sem_virt.html">résumés</a>)
+ </li>
+ <li> <a href="manif/jnbioss_201911/jnbioss_201911.html">Journée annuelle du GT Bioss</a><br>
+ Dates : 7 novembre 2019<br>
+ Lieu : Université Paris Diderot, France.<br>
+ </li>
+ <li> <a href="https://jobim2019.sciencesconf.org/resource/page/id/5">Session thématiques "Predictive approaches for biological systems engineering" de JOBIM 2019</a><br>
+ Dates : 3 juillet (après-midi) 2019<br>
+ Lieu : Nantes, France. <br>
+ </li>
+ <li> <a href="manif/nantes_201907/nantes_201907.html">Journées thématiques "Médecine Personnalisée"</a><br>
+ Dates : 1er et 2 juillet 2019<br>
+ Lieu : Nantes, France. <br>
+ </li>
+ <li> <a href="manif/biossia_201905/biossia201905.html">Journée Bioss-IA</a><br>
+ Dates : 27 mai 2019<br>
+ Lieu : Orléans, France. <br>
+ </li>
+ <li> <a href="manif/biossia_201812/biossia201812.html">Journées Bioss-IA</a><br>
+ Dates : 18 et 19 décembre 2018<br>
+ Lieu : Paris, France. <br>
+ </li>
+ <li> <a href="https://perso.crans.org/~genest/asstabio.html">ASSTABIO: Apprentissage de modèles Statistiques et STochastiques A partir de données BIOlogiques</a><br>
+ Dates : 22 et 23 mars 2018<br>
+ Lieu : Rennes, France. <br>
+ </li>
+ <li> <a href="https://www.lri.fr/~pauleve/bioss-ia-0617/">Journées thématiques "Modèles logiques pour la représentation formelle des systèmes vivants"</a><br>
+ Dates : 22 et 23 Juin 2017<br>
+ Lieu : Gif-sur-Yvette, France. <br>
+ </li>
+ <li> <a href="manif/jnbioss_201703/jnbioss201703.html">3ème édition des journées annuelles</a><br>
+ Dates : 13 et 14 Mars 2017<br>
+ Lieu : Montpellier, France. <br>
+ </li>
+ <li><font color="#b40404"><a href="manif/lille_201611/lille201611.html">Journées
+ thématiques "Métabolisme"</a></font><br>
+ Dates : 24 et 25 novembre 2016<br>
+ Lieu : Lille, France. </li>
+ <li> <font color="#b40404"> <a href="manif/jnbioss_201607/jnbioss201607.html">2ème
+ édition des journées annuelles</a></font><br>
+ Dates : 1er et 2 juillet 2016<br>
+ Lieu : Lyon, France. </li>
+ <li> <font color="#b40404"> <a href="manif/jnbioss_201511/jnbioss201511.html">1ère
+ édition des journées annuelles</a></font><br>
+ Dates : 23 novembre 2015<br>
+ Lieu : Bât. Sophie Germain, Université Paris Diderot,
+ Paris, France </li>
+ <li> <font color="#b40404"> <a href="manif/nantes_201509/nantes201509.html"> <i>1st
+ workshop</i> Bioss</a></font> associé à la
+ conférence <a href="http://cmsb2015.sciencesconf.org">CMSB
+
+
+
+ 2015</a><br>
+ Dates : 18 septembre 2015<br>
+ Lieu : Centre des congrès, Nantes, France </li>
+ <li> <font color="#b40404"> <a href="manif/cirm_201505/cirm201505.html">Journées
+ thématiques "Méthodes de réduction de modèles
+ discrets"</a> </font><br>
+ Dates : 28 et 29 mai 2015<br>
+ Lieu : Centre international de rencontres mathématiques
+ (CIRM), Marseille, France </li>
+ </ul>
+ <h2>Autres manifestations</h2>
+ <h3>Conférences</h3>
+ <ul>
+ <li> ISMB/ECCB 2017 - Intelligent Systems for Molecular
+ Biology / European Conference on Computational Biology<br>
+ Dates : 21 - 25 juillet 2017<br>
+ Lieu : Prague, République tchèque<br>
+ url : https://www.iscb.org/ismbeccb2017 </li>
+ <li> JOBIM 2017 - Journées ouvertes biologie, informatique
+ et mathématiques<br>
+ Dates : 3 - 6 juillet 2017<br>
+ Lieu : Lille, France<br>
+ url : à venir </li>
+ <li> CMSB 2016 - Computational Methods for Systems Biology<br>
+ Dates : 21 - 23 Septembre 2015<br>
+ Lieu : Cambridge, UK<br>
+ url : https://www.cl.cam.ac.uk/events/cmsb2016/ </li>
+ <li> DNA 2016 - DNA Computing and Molecular Programming<br>
+ Dates : 4 - 8 septembre 2016<br>
+ Lieu : Munich, Allemagne<br>
+ url : http://www.dna-node.com/dna22/ </li>
+ <li> ECCB 2016 - European Conference on Computational
+ Biology<br>
+ Dates : 3 - 7 septembre 2016<br>
+ Lieu : La Haye, Pays-Bas<br>
+ url : http://www.eccb2016.org </li>
+ <li> JOBIM 2016 - Journées ouvertes biologie, informatique
+ et mathématiques<br>
+ Dates : 28 - 30 juin 2016<br>
+ Lieu : Lyon, France<br>
+ url : http://jobim2016.sciencesconf.org </li>
+ <li> Séminaire de la SFBT 2016<br>
+ Dates : 12 - 15 juin 2016<br>
+ Lieu : Saint-Flour, France<br>
+ url : http://sfbt16.mio.univ-amu.fr </li>
+ <li> Journées nationales du GDR IM<br>
+ Dates : 18 - 20 janvier 2016<br>
+ Lieu : Villetaneuse, France<br>
+ url : https://lipn.univ-paris13.fr/GDR-IM-2016/ </li>
+ <li> Colloque 2015 du GDR BiM<br>
+ Dates : 7 - 8 octobre 2015<br>
+ Lieu : Paris, France<br>
+ url : http://www.gdr-bim.cnrs.fr/?p=187 </li>
+ <li> CMSB 2015 - Computational Methods for Systems Biology<br>
+ Dates : 16 - 18 Septembre 2015<br>
+ Lieu : Nantes, France<br>
+ url : http://cmsb2015.sciencesconf.org </li>
+ <li> DNA 2015 - DNA Computing and Molecular Programming<br>
+ Dates : 17 - 21 août 2015<br>
+ Lieu : Boston/Cambridge, USA<br>
+ url : http://projects.iq.harvard.edu/dna21/home </li>
+ <li> ISMB/ECCB 2015 - Intelligent Systems for Molecular
+ Biology / European Conference on Computational Biology<br>
+ Dates : 10 - 14 Juillet 2015<br>
+ Lieu : Dublin, Irlande<br>
+ url : http://www.iscb.org/ismbeccb2015 </li>
+ <li> JOBIM 2015 - Journées ouvertes biologie, informatique
+ et mathématiques<br>
+ Dates : 6 - 9 juillet 2015<br>
+ Lieu : Clermont-Ferrand, France<br>
+ url : https://www6.inra.fr/jobim2015 </li>
+ <li> Séminaire de la SFBT 2015<br>
+ Dates : 22 - 25 juin 2015<br>
+ Lieu : Poitiers, France<br>
+ url : http://www-math.sp2mi.univ-poitiers.fr/SFBT2015/fr/
+ </li>
+ <li> PESB 2015 - Perspectives in Environmental and Systems
+ Biology<br>
+ Dates : 13 - 15 avril 2015<br>
+ Lieu : Grenoble, France<br>
+ url : http://www.beesy2015.com </li>
+ <li> Journées nationales du GDR IM<br>
+ Dates : 2 - 3 février 2015<br>
+ Lieu : Bordeaux, France<br>
+ url : http://gdrim2015.labri.fr/index.php </li>
+ <li> CMSB 2014 - Computational Methods for Systems Biology<br>
+ Dates : 17 - 19 novembre 2014<br>
+ Lieu : Manchester, United Kingdom<br>
+ url : http://www.comp-sys-bio.org/CMSB14 </li>
+ <li> DNA 2014 - DNA Computing and Molecular Programming<br>
+ Dates : 22 - 25 septembre 2014<br>
+ Lieu : Kyoto, Japon<br>
+ url : http://dna20.molecular-robotics.org </li>
+ <li> FMMB 2014 - Formal Methods in Macro-Biology<br>
+ Dates : 22 - 24 Septembre 2014<br>
+ Lieu : Nouméa, Nouvelle Calédonie<br>
+ url : http://fmmb2014.sciencesconf.org </li>
+ <li> ECCB 2014 - European Conference on Computational
+ Biology<br>
+ Dates : 7 - 10 septembre 2014<br>
+ Lieu : Strasbourg, France<br>
+ url : http://www.eccb14.org/ </li>
+ <li> JOBIM 2014 - Journées ouvertes biologie, informatique
+ et mathématiques<br>
+ Dates : 7 - 10 septembre 2014<br>
+ Lieu : Strasbourg, France<br>
+ url : http://www.eccb14.org/ </li>
+ <li> Journées nationales du GDR IM<br>
+ Dates : 29 - 30 janvier 2014<br>
+ Lieu : Paris, France<br>
+ url : https://www.gdr-im.fr/?q=node/38 </li>
+ </ul>
+ <h3>Journées thématiques et groupes de travail</h3>
+ <ul>
+ <li> Groupe de travail "Réseaux d'interactions - fondements
+ et applications à la biologie"<br>
+ Dates : 3 - 6 janvier 2017<br>
+ Lieu : Marseille, France<br>
+ url : à venir </li>
+ <li> SASB 2016 - Workshop on Static Analysis and Systems
+ Biology<br>
+ Dates : 7 septembre 2016<br>
+ Lieu : Édimbourg, Royaume-Uni<br>
+ url : http://sasb2016.fi.muni.cz </li>
+ <li> BioPPN 2016 - Workshop on Biological Processes &
+ Petri Nets<br>
+ Dates : 20 juin 2016<br>
+ Lieu : Torun, Pologne<br>
+ url :
+ http://www-dssz.informatik.tu-cottbus.de/BME/BioPPN2016 </li>
+ <li> SASB 2015 - Workshop on Static Analysis and Systems
+ Biology<br>
+ Dates : 8 septembre 2015<br>
+ Lieu : Saint-Malo, France<br>
+ url : https://www.lri.fr/sasb2015 </li>
+ <li> BioPPN 2015 - Workshop on Biological Processes &
+ Petri Nets<br>
+ Dates : 22 juin 2015<br>
+ Lieu : Bruxelles, Belgique<br>
+ url :
+ http://www-dssz.informatik.tu-cottbus.de/BME/BioPPN2015 </li>
+ <li> Groupe de travail "Théorie des réseaux booléens"<br>
+ Dates : 4 - 7 novembre 2014<br>
+ Lieu : Nice, France<br>
+ url :
+ http://www.i3s.unice.fr/workshop_reseaux_booleens_2014 </li>
+ <li> SASB 2014 - Workshop on Static Analysis and Systems
+ Biology<br>
+ Dates : 10 septembre 2014<br>
+ Lieu : Munich, Allemagne<br>
+ url : https://www.lri.fr/sasb2014 </li>
+ <li> LMACN 2014 - Workshop on Logical Modelling and Analysis
+ of Cellular Networks<br>
+ Dates : 6 septembre 2014<br>
+ Lieu : Strasbourg, France<br>
+ url : http://www.eccb14.org/program/workshops/lmacn </li>
+ </ul>
+ <h3>Écoles</h3>
+ <ul>
+ <li> SSBSS 2017 - International Synthetic and Systems
+ Biology Summer School<br>
+ Dates : 17 - 21 juillet 2017<br>
+ Lieu : Cambridge, Royaume-Uni<br>
+ url : http://www.taosciences.it/ssbss-2017 </li>
+ <li> SSBSS 2016 - International Synthetic and Systems
+ Biology Summer School<br>
+ Dates : 8 - 14 juillet 2016<br>
+ Lieu : Volterra (Pise), Italie<br>
+ url : http://www.taosciences.it/ssbss </li>
+ <li> École "Modélisation formelle de réseaux de régulation
+ biologique"<br>
+ Dates : 6 - 10 juin 2016<br>
+ Lieu : île de Porquerolles, France<br>
+ url : http://i3s.unice.fr/bioregul/ </li>
+ <li> SSBSS 2015 - International Synthetic and Systems
+ Biology Summer School<br>
+ Dates : 5 - 9 juillet 2015<br>
+ Lieu : Taormine, Italie (Sicile)<br>
+ url : http://www.taosciences.it/ssbss2015/ </li>
+ <li> Advanced Lecture Course on Computational Systems
+ Biology<br>
+ Dates : 6 - 11 avril 2015<br>
+ Lieu : Aussois, France<br>
+ url : http://compsysbio.inria.fr </li>
+ <li> aSSB 2015 - Advances in Systems and Synthetic Biology<br>
+ Dates : 23 - 27 mars 2015<br>
+ Lieu : Strasbourg, France<br>
+ url : http://epigenomique.free.fr/en/index.php </li>
+ </ul>
+ </div>
+ <!-- <div id="banner"></div> --> </div>
+ <div id="footer">
+ <p><a href="index.html">Accueil</a> | <a href="membres.html">Membres</a>
+ | <a href="manif.html">Manifestations</a> | <a href="projets.html">Projets</a> | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+
+
+
+</body></html>
diff --git a/manif/biossia_201812/biossia201812.html b/manif/biossia_201812/biossia201812.html
new file mode 100644
index 0000000000000000000000000000000000000000..ab66e091e72f4eacb3f3d36c49773ab90af4953c
--- /dev/null
+++ b/manif/biossia_201812/biossia201812.html
@@ -0,0 +1,152 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
+
+<body>,
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li><a href="../../index.html">Accueil</a></li>
+ <li><a href="../../membres.html">Membres</a></li>
+ <li><a href="../../manif.html">Manifestations</a></li>
+ <li><a href="../../projets.html">Projets</a></li>
+ <li><a href="../../contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées Bioss-IA (2ème édition)</h1>
+ <img src="./gdria.jpg" height="60"/>
+ <img src="./logo-gdria.jpg" height="60"/>
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ <b>Journées financées par le <a href="https://www.gdria.fr/">GDR IA</a>, Bioss et Psay CompBio (Paris-Saclay), avec remerciements
+ à l’institut Pasteur pour le prêt de la salle et à Denise Derhy
+ et Gregory Batt pour l’aide en local à l’organisation.</b>
+ </p>
+ <p align="justify">
+ <b>Date</b> : 18 décembre après-midi et 19 décembre matin 2018
+ </p>
+ <p align="justify">
+ <b>Lieu</b> : grande salle du Bât Lwoff (Rétrovirus 14), Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris.
+ <b>Se munir d'une pièce d'identité</b>
+ </p>
+
+ <p align="justify">
+ <b>Organisateur</b> : Philippe Dague
+ </p>
+ <h3>Mardi 18 décembre</h3>
+
+ 13h30-14h00 Accueil<br/>
+ 14h00-14h45 Adrien Husson : Transition logic for rule-based biology: a tractable, hybrid specification framework<br/>
+ 14h45-15h30 Andrei Doncescu, Vincent Risch, Pierre Siegel : Quelques formalismes et algorithmes d’IA pour les réseaux biologiques<br/>
+ 15h30-16h15 Loïc Paulevé : Reconciling qualitative and abstract (and scalable) reasoning with Boolean networks<br/>
+ 16h15-16h45 Pause<br/>
+ 16h45-17h30 Laurent Trilling : Constraint ASP Based Inference of Delay Parameters for Discrete Genetic Networks<br/>
+ 17h30-18h15 Jérôme Feret : Conservative approximations of models of polymers<br/>
+ 20h00 Dîner (restaurant) <br/><br/>
+
+
+ <h3>Mercredi 19 décembre</h3>
+ 9h00-9h15 Accueil<br/>
+ 9h15-10h00 Marie Beurton-Aimar : Introduction to Deep Learning and its Tools applied to Biology<br/>
+ 10h00-10h45 Hervé Isambert : Learning causal and non-causal networks from large scale genomic and clinical data<br/>
+ 10h45-11h15 Pause<br/>
+ 11h15-12h00 Ovidiu Radulescu : Hybrid symbolic-numeric learning of Markov eukaryotic promoters models<br/>
+ 12h00-12h45 Engelbert Mephu Nguifo : A Novel Computational Approach for Global Alignment for Multiple Biological Networks<br/>
+ 12h45-13h45 Déjeuner (plateaux repas)<br/>
+ 13h45-14h30 Eugenio Cinquemani : Towards automated control of synthetic E.coli communities<br/>
+ 14h30-15h15 Vincent Danos : Stochastic analysis of reaction-division systems<br/>
+ 15h15-16h00 Maxime Folschette : Search of Therapeutic Targets on the Hepatocellular Carcinoma with Database Extraction and Graph Coloring Methods<br/>
+ 16h00-16h30 Julien Martinelli : Unsupervised learning of chemical reaction networks from data time series<br/>
+
+
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
+ </div>
+
+ <div id="content_footer"></div>
+
+ <div id="footer">
+ <p><a href="../../index.html">Accueil</a>
+ | <a href="../../membres.html">Membres</a>
+ | <a href="../../manif.html">Manifestations</a>
+ | <a href="../../projets.html">Projets</a>
+ | <a href="../../contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+
+
+</body></html>
diff --git a/manif/biossia_201812/gdria.jpg b/manif/biossia_201812/gdria.jpg
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diff --git a/manif/biossia_201905/biossia201905.html b/manif/biossia_201905/biossia201905.html
new file mode 100644
index 0000000000000000000000000000000000000000..cc5caea417bebd4faf798e47370a25c441cd172b
--- /dev/null
+++ b/manif/biossia_201905/biossia201905.html
@@ -0,0 +1,151 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
+
+<body>,
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li><a href="../../index.html">Accueil</a></li>
+ <li><a href="../../membres.html">Membres</a></li>
+ <li><a href="../../manif.html">Manifestations</a></li>
+ <li><a href="../../projets.html">Projets</a></li>
+ <li><a href="../../contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées Bioss-IA (3ème édition)</h1>
+ <img src="./gdria.jpg" height="60"/>
+ <img src="./logo-gdria.jpg" height="60"/>
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ Cette journée aura lieu en préambule des <a href="https://gdria2019.sciencesconf.org/">journées plénières</a> du GDR IA les 28 et 29 mai
+ <p align="justify">
+ <b>Date</b> : 27 mai 2019
+ </p>
+ <p align="justify">
+ <b>Lieu</b> : LIFO, campus universitaire La Source, Orléans.
+ </p>
+
+ <p align="justify">
+ <b>Organisateur</b> : Philippe Dague
+ </p>
+ <p align="justify">
+ <b>Inscription</b> : gratuite mais OBLIGATOIRE SUR LE SITE DU GDR IA : <a href="https://gdria2019.sciencesconf.org/">https://gdria2019.sciencesconf.org/</a> (même si vous ne participez qu'à la journée du 27 mai).
+ </p>
+ <h3>Exposés</h3>
+ <ul>
+ <li> 10h-10h45 : Accueil et café </li>
+ <li> 10h45-12h :
+ <ul>
+ <li> Bishnu Sarker : Exploiting Complex Protein Domain Networks for Protein Function Annotation (45mn) </li>
+ <li> Adrien Husson : Spécification de système de transition : borner les changements par déduction (30mn) </li>
+ </ul>
+ </li>
+ <li> 12h-14h : Repas </li>
+ <li> 14h-15h30 :
+ <ul>
+ <li> Marie Beurton-Aimar : Traitement d'images - IRM de cerveau - par deep learning pour du diagnostic (45mn) </li>
+ <li> Alexandra Fronville : Analyse de la niche à cellules souches cancéreuses par des méthodes de deep learning (45mn) </li>
+ </ul>
+ </li>
+ <li> 15h30-16h : Pause café</li>
+ <li> 16h-17h30 :
+ <ul>
+ <li> Julien Martinelli : A Statistical Learning Algorithm for Inferring Reaction Systems from Data Time Series (45mn) </li>
+ <li> Hervé Isambert : Learning clinical networks from medical records based on information estimates in mixed-type data (45mn) </li>
+ </ul>
+ </li>
+ <li> 17h30-18h : Discussion </li>
+ <li> 19h30-22h30 : Session doctorants </li>
+ </ul>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
+ </div>
+
+ <div id="content_footer"></div>
+
+ <div id="footer">
+ <p><a href="../../index.html">Accueil</a>
+ | <a href="../../membres.html">Membres</a>
+ | <a href="../../manif.html">Manifestations</a>
+ | <a href="../../projets.html">Projets</a>
+ | <a href="../../contact.html">Contact</a></p>
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+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
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+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
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+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées Bioss-IA (4ème édition)</h1>
+ <img src="./gdria.jpg" height="60"/>
+ <img src="./logo-gdria.jpg" height="60"/>
+ <p align="justify">
+ <b>Date</b> : 24 novembre 2020
+ </p>
+ <p align="justify">
+ <b>Lieu</b> : virtuel
+ </p>
+
+ <p align="justify">
+ <b>Organisateur</b> : Philippe Dague
+ </p>
+
+ <p><a href="https://drive.google.com/drive/folders/11Fnr6gWJi9NCrOeiStpU3nKr5pa9D796?usp=sharing">enregistrements des exposés</a> </p>
+
+ 9h30-10h15 -- <b>Thomas Schiex</b> : <i>AI for fun and serious puzzles: bridging symbolic and numerical reasoning to learn how to solve the Sudoku or design new protein</i> (<a href="./slides/Schiex.pdf">slides</a>) <br/>
+
+ 10h15-11h -- <b>Emmanuel Bouilhol</b> (travail avec Macha Nikolski) : <i>Deep Learning approaches for reliable quantification of multi-omics cell imaging datasets to interrogate RNA and protein spatial and temporal subcellular interactions</i>(<a href="./slides/Bouilhol.pdf">slides</a>)<br/>
+
+ 11h15-12h00 -- <b>Maxime Folschette</b> (travail avec Morgan Magnin et Tony Ribeiro) : <i>GULA: Learning (From Any) Semantics of a Biological Regulatory Network</i> (<a href="./slides/Folschette.pdf">slides</a>)<br/>
+
+ 12h00-12h45 -- <b>Anna Niarakis</b> (travail avec John Bachman, Angela Bauch, Benjamin Gyori, Dieter Maier, Marek Ostaszewski) : <i>AI-assisted human biocuration of molecular mechanisms in the COVID-19 Disease Map project</i> (<a href="./slides/Niarakis.pdf">slides</a>)<br/>
+
+ 13h45-14h30 -- <b>Cédric Gaucherel</b> : <i>A formal language for ecosystems</i> (<a href="./slides/Gaucherel.pdf">slides</a>)<br/>
+
+ 14h30-15h15 -- <b> Jean Krivine</b> (travail avec Adrien Husson) : <i>Logics as Knowledge assembly code for Molecular Biology</i> (<a href="./slides/Krivine.pdf">slides</a>) <br/>
+
+ 15h15-16h -- <b>Sergiu Ivanov</b> (travail avec Nicolas Glade, Rémi Segretain et Laurent Trilling) : <i>A Methodology for Evaluating the Extensibility of Boolean Networks’ Structure and Function</i> (<a href="./slides/Ivanov.pdf">slides</a>)<br/>
+
+ 16h15-17h -- <b>Tarek Khaled</b> (travail avec Belaid Benhamou) : <i>Une approche basée sur l'ASP pour la représentation des réseaux booléens et la détection des attracteurs : application aux réseaux de gènes</i> (<a href="./slides/Khaled.pdf">slides</a>)<br/>
+
+ 17h-17h45 -- <b>Pierre Siegel</b> (travail avec Andrei Doncescu, Vincent Risch et Sylvan Sené) : <i>Systèmes Dynamiques Booléens et algorithmes basés sur SAT</i> (<a href="./slides/Siegel.pdf">slides</a>)<br/>
+
+ 17h45-18h00 -- <b>Discussion - Perspectives</b><br/>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
+ </div>
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+
+ <div id="footer">
+ <p><a href="../../index.html">Accueil</a>
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@@ -0,0 +1,494 @@
+<!DOCTYPE HTML>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html;
+ charset=utf-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
+
+<body>
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li><a href="../../index.html">Accueil</a></li>
+ <li><a href="../../membres.html">Membres</a></li>
+ <li><a href="../../manif.html">Manifestations</a></li>
+ <li><a href="../../projets.html">Projets</a></li>
+ <li><a href="../../contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées thématiques "Méthodes de réduction de modèles"</h1>
+
+ <h3>Thème de la réunion</h3>
+ <p align="justify">
+ La modélisation et l’étude des réseaux biomoléculaires est
+ un domaine en pleine expansion, et les modèles considérés
+ sont de taille et complexité croissantes. C’est pourquoi de
+ plus en plus d’attention et d’efforts sont portés sur les
+ techniques et méthodes de réduction de modèles. Le choix de
+ la méthode de réduction va bien évidemment dépendre du
+ formalisme de modélisation utilisé, du système biologique
+ étudié, des données biologiques accessibles, etc... Un des
+ critères fondamentaux dans le choix de la méthode de
+ réduction est l’ensemble des propriétés dynamiques qui sont
+ conservées : en particulier, les attracteurs, la
+ fonctionnalité des circuits, les échelles de temps, et les
+ comportements transitoires peuvent être impactés lors de la
+ réduction du système, ce qui peut nuire à l’interprétation
+ biologique des propriétés du systèmes. Cet atelier a pour
+ but de faire le point sur les différentes approches
+ utilisées dans le cadre de la réduction de modèles
+ biologiques discrets. Ainsi, les participants pressentis ont
+ des expériences d’étude et d’utilisation de méthodes de
+ réductions dans divers types de réseaux biologiques (réseaux
+ de régulation, réseaux métaboliques...), ayant recours à
+ différents formalismes de modélisation (réseaux d’automates,
+ réseaux booléens, modélisation à base de règles, réseaux
+ d’interactions...). L’objectif sera de mieux comprendre les
+ différentes approches existantes, les invariants conservés
+ par ces approches, ainsi que les applications transversales
+ possibles à différents formalismes.
+ </p>
+
+ <h3>Programme</h3>
+ <h4>Jeudi 28 mai 2015</h4>
+ <p align="left">
+ <b>10h15 - 10h30</b> -
+ <a href="siegel.pdf">slides</a><br>
+ Anne Siegel - <i>Présentation du GT
+ Bioss</i><br>
+ <b>10h30 - 11h15</b> -
+ <a href="feret.pdf">slides</a><br>
+ Jérôme Feret - <i>Réduction de modèles de
+ voies de signalisation intracellulaires</i><br>
+ <b>11h15 - 12h00</b> -
+ <a href="radulescu.pdf">slides</a><br>
+ Ovidiu Radulescu - <i>Taming the complexity of
+ biochemical networks through model reduction and tropical
+ geometry</i><br>
+ <b>14h00 - 14h45</b> -
+ <a href="richard.pdf">slides</a><br>
+ Adrien Richard - <i>Reduction of finite
+ dynamical systems and linear network coding solvability</i><br>
+ <b>14h45 - 15h30</b> -
+ <a href="eveillard.pdf">slides</a><br>
+ Damien Éveillard - <i>Rechercher des modules
+ dans les réseaux métaboliques</i><br>
+ <b>16h00 - 16h45</b> -
+ <a href="madelaine.pdf">slides</a><br>
+ Guillaume Madelaine - <i>Structural
+ simplification of chemical reaction networks preserving deterministic
+ semantics</i><br>
+ <b>16h45 - 17h30</b> -
+ <a href="basso.pdf">slides</a><br>
+ Adrien Basso-Blandin - <i>Modèle de
+ représentation de connaissances annoté pour la biologie</i><br>
+ <b>17h30 - 18h15</b> -
+ <a href="pauleve.pdf">slides</a><br>
+ Loïc Paulevé - <i>Goal-oriented reduction of
+ automata networks</i><br>
+ </p>
+
+ <h4>Vendredi 29 mai 2015</h4>
+ <p align="left">
+ <b>09h00 - 09h45</b> -
+ <a href="fages.pdf">slides</a><br>
+ François Fages - <i>Réductions de modèles par
+ épimorphismes de sous-graphes</i><br>
+ <b>09h45 - 10h30</b> -
+ <a href="delaplace.pdf">slides</a><br>
+ Franck Delaplace - <i>Analogous dynamics of
+ Boolean networks</i><br>
+ <b>11h00 - 11h45</b> -
+ <a href="naldi.pdf">slides</a><br>
+ Aurélien Naldi - <i>Une méthode de réduction
+ pour la manipulation de grands modèles logiques</i><br>
+ <b>11h45 - 12h30</b> -
+ <a href="abou_jaoude.pdf">slides</a><br>
+ Wassim Abou-Jaoudé - <i>Derivation of dynamical
+ qualitative models from biochemical networks</i><br>
+ <b>14h00 - 14h45</b> -
+ <a href="melliti.pdf">slides</a><br>
+ Tarek Melliti - <i>Analysis of modular
+ organisation of interaction networks based on asymptotic
+ dynamics</i><br>
+ <b>14h45 - 15h30</b> -
+ <a href="tournier.pdf">slides</a><br>
+ Laurent Tournier - <i>Uncovering regulations in
+ B. subtilis metabolic network, combining optimal resource allocation
+ and Boolean inference</i><br>
+ </p>
+
+ <h3>Résumés</h3>
+ <p align="justify">
+ <b>Wassim Abou-Jaoudé</b> - <i>Derivation of dynamical qualitative
+ models from biochemical networks</i><br>
+ As technological advances allow a better identification of cellular
+ networks, more and more molecular data are produced allowing the
+ construction of detailed molecular interaction maps. One strategy to
+ get insights into the dynamical properties of such systems is to
+ derive compact dynamical models from these maps, which would then be
+ handled more efficiently for the analysis of their dynamics.
+ Starting from two specific case studies of networks, I will present a
+ methodology for the derivation of qualitative dynamical models from
+ biochemical networks. Properties are formalised using abstraction
+ interpretation techniques. We first abstract states and traces by
+ quotienting the state space by intervals. The induced abstract
+ semantics is too coarse to reproduce the properties of interest for
+ our two examples. We then refine the abstract semantics by introducing
+ additional constraints and information on the kinetics computed by
+ abstract interpretation. The resulting semantics is able to reproduce
+ our properties of interest.<br><br>
+ <b>Adrien Basso-Blandin</b> - <i>Modèle de représentation de
+ connaissances annoté pour la biologie</i><br>
+ L'étude des voies de signalisations biologiques des cancers est un
+ travail extrêmement complexe. En effet, de tels systèmes possèdent de
+ nombreux paramètres, agents et processus, mais ils sont étudiés par
+ fragments et leurs littératures et données sont fragmentées,
+ distribuées et parfois contradictoires. Il est difficile de construire
+ les modèles complets, explicatifs de tels systèmes complexes et des
+ interactions dans ces systèmes qui sont provoquées par beaucoup de
+ facteurs interagissants de manière peu connue ou mal comprise.
+ Les "Big mechanisms" sont de grands modèles explicatifs de systèmes
+ complexes au sein desquels les interactions ont des effets causals
+ importants. La collection de données de masse est de plus en plus
+ automatisée, néanmoins, la création de "Big mechanisms" reste un
+ processus manuel de plus en plus difficile à réaliser de par la
+ fragmentation et la distribution de connaissances. L'automatisation de
+ la conception de ces "Big mechanisms" permettrait une évolution
+ majeure pour la science et la façon dont la recherche est
+ réalisée.
+ Ici nous introduisons d'un coté un modèle de représentation de
+ connaissance pour la biologie afin d'intégrer plus ou moins
+ immédiatement et automatiquement (ou "semi automatiquement") au sein
+ de modèles causals explicatifs, des connaissances biologiques
+ extraites de la littérature. Combiné à ce modèle, nous proposons une
+ traduction automatique de cette représentation de connaissances en
+ modèles Kappa.<br><br>
+ <b>Franck Delaplace</b> - <i>Analogous dynamics of Boolean
+ networks</i><br>
+ Different Boolean networks may reveal similar dynamics although their
+ definition differs, then preventing their distinction from the
+ observations. This raises the question about the sufficiency of a
+ particular Boolean network for properly reproducing a modeled
+ phenomenon to make realistic predictions. The question actually
+ depends on the invariant properties of behaviorally similar Boolean
+ networks. We address this issue by considering that the similarity is
+ formalized by isomorphism on graphs modeling their dynamics. The
+ similarity also depends on the parameter governing the updating
+ policy, called the "mode". We define a general characterization of the
+ group of isomorphism preserving the mode. From this characterization,
+ we deduce invariant structural properties of the interaction graph and
+ conditions to maintain an equivalence through mode variation.<br><br>
+ <b>Damien Éveillard</b> - <i>Rechercher des modules dans les réseaux
+ métaboliques</i><br>
+ Les récents progrès biotechnologiques permettent de reconstruire
+ des réseaux métaboliques à l’échelle des génomes pour notamment
+ appliquer les approches de type FBA. Cependant, au delà de l’intérêt
+ de ces approches pour prédire des comportements quantitatifs,
+ l’analyse per se des réseaux métaboliques reste difficile. Cette
+ difficulté est particulièrement d’actualité pour analyser le réseau
+ métabolique qui résultent d’interactions microbiennes, et qui mettent
+ en oeuvre différents réseaux métaboliques issus des espèces
+ bactériennes en présence. Cet exposé propose de surmonter cette
+ difficulté avec la recherche des modules de flux. Cette technique
+ analyse le réseau métabolique en décomposant l’espace de solutions
+ optimales. Nous montrerons, après application sur deux systèmes de
+ référence, que cette décomposition est biologiquement intéressante
+ biologique et qu’elle ouvre de riches perspectives méthodologiques
+ pour la modélisation des réseaux métaboliques.<br><br>
+ <b>François Fages</b> - <i>Réductions de modèles par épimorphismes de
+ sous-graphes</i><br>
+ Nous proposons un cadre de réduction de modèles, basé uniquement sur
+ des graphes, qui permet d'organiser les bases de modèles en un ordre
+ partiel. Pour capturer le processus de réduction lui-même, nous
+ utilisons un type particulier de morphismes de graphes: les
+ épimorphismes de sous-graphes, qui permettent la fusion et
+ l'effacement de sommets. Nous commencerons en analysant l'ordre
+ partiel qui émerge des opérations de fusion et d'effacement,
+ montrerons sa complexité théorique et sa résolution pratique en
+ programmation par contraintes, et évaluerons les performances et la
+ précision de cette approche sur la base de modèles BioModels. Enfin,
+ nous discuterons de nos travaux en cours sur la recherche de motifs
+ dans les réseaux de réactions protéiques, ainsi que sur la prise en
+ compte des critères dynamiques par des méthodes d'algèbre
+ tropicale.<br><br>
+ <b>Jérôme Feret</b> - <i>Réduction de modèles de voies de
+ signalisation intracellulaire</i><br>
+ Les voies de signalisation intracellulaire sont des cascades
+ d'interaction entre protéines, qui permettent à la cellule de recevoir
+ des signaux, de les propager jusqu'à son noyau, puis de les intégrer,
+ ce qui, in fine, influe sur le comportement global de la cellule. Les
+ protéines s'associent entre elles sur des sites de liaisons, puis
+ modifient la structure spatiale de leurs voisines, ce qui a pour effet
+ de cacher ou de découvrir leurs autres sites de liaisons, et donc
+ d'empêcher ou de faciliter d'autres interactions. De vastes bases de
+ données ont été conçues pour répertorier les différentes interactions
+ connues entre les sites des protéines. Cependant, nous ne savons
+ toujours pas clairement comment les propriétés physiologiques de la
+ cellule émergent de ces interactions. La difficulté principale est la
+ grande combinatoire de ces modèles. En effet, chaque protéine a
+ beaucoup de sites de liaisons. Ainsi, un très grand nombre de
+ complexes biomoléculaires différents peut se former. Pour décrire ces
+ modèles, nous proposons d'utiliser des graphes pour la représentation
+ des complexes biomoléculaires et des règles de réécritures pour la
+ spécification des interactions entre les protéines. En particulier,
+ ces règles sont contextuelles : elles décrivent non seulement les
+ transformations sur les complexes biomoléculaires, mais aussi les
+ conditions nécessaires à ces transformations. Ceci offre une
+ représentation très compacte et pratique d'un modèle. Par ailleurs ces
+ règles permettent de formaliser le comportement des modèles à
+ différents niveaux d'abstraction (qualitatifs ou quantitatifs).
+ Malheureusement, l'écueil de la complexité combinatoire refait surface
+ lorsque l'on cherche à calculer de manière effective ce
+ comportement.<br>
+ Nous proposons une méthode pour réduire la taille des systèmes
+ différentiels qui décrivent le comportement de ces modèles. Nous
+ utilisons une analyse du flot d'information entre les différents sites
+ des complexes biomoléculaires. Ainsi, pour chaque site de liaison
+ d'un complexe biomoléculaire, nous détectons quelles sont les parties
+ de ce complexe qui peuvent influencer la capacité de lier ou de délier
+ ce site. Nous en déduisons des paires de sites dont on peut abstraire
+ la relation entre l'état de liaison, car les ensembles de sites
+ qu'ils peuvent influencer sont disjoints. Cela nous permet de découper
+ les espèces biomoléculaires en plus petits morceaux (en séparant de
+ telles paires de sites). Nous obtenons ainsi un système différentiel
+ portant sur la concentration de ces morceaux de complexes
+ biomoléculaires, qui sont beaucoup moins nombreux que les complexes
+ biomoléculaires du système différentiel du modèle initial, et ce sans
+ jamais avoir écrit explicitement ce système initial. Pourtant, notre
+ méthode de réduction est exacte : nous avons la preuve que la solution
+ du système obtenu, est la projection exacte de la solution du système
+ initial.<br><br>
+ <b>Guillaume Madelaine</b> - <i>Structural simplification of chemical
+ reaction networks preserving deterministic semantics</i><br>
+ We study the structural simplification of chemical reaction networks
+ preserving the deterministic kinetics. We aim at finding
+ simplification rules that can eliminate intermediate molecules while
+ preserving the dynamics of all others. The rules should be valid even
+ though the network is plugged into a bigger context. An example is
+ Michaelis-Menten’s simplification rule for enzymatic reactions. In
+ this paper, we present a large class of structural simplifications
+ rules for reaction networks that can eliminate intermediate molecules
+ at equilibrium, without assuming that all molecules are at
+ equilibrium, i.e. in a steady state. We prove the correctness of our
+ simplification rules for all contexts that preserve the equilibrium of
+ the eliminated molecule. Finally, we illustrate at a concrete example
+ networks from systems biology, that our simplification rules may allow
+ to drastically reduce the size of reaction networks in
+ practice.<br><br>
+ <b>Tarek Melliti</b> - <i>Analysis of modular organisation of
+ interaction networks based on asymptotic dynamics</i><br>
+ We will present a work related to modularity in biological interaction
+ networks, for which has been developped a discrete theoretical
+ framework based on the analysis of the asymptotic dynamics of
+ biological interaction networks. More precisely, we will exhibit
+ formal conditions under which agents of interaction networks can be
+ grouped into modules, forming a modular organisation. We will see that
+ the conventional decomposition into strongly connected components
+ fulfills the formal conditions of being a modular organisation. We
+ will also propose a modular and incremental algorithm for an
+ efficient equilibria computation. Furthermore, we will point out that
+ our framework enables a finer analysis providing a decomposition into
+ elementary modules, possibly smaller than strongly connected
+ components.<br><br>
+ <b>Aurélien Naldi</b> - <i>Une méthode de réduction pour la
+ manipulation de grands modèles logiques</i><br>
+ Nous avons proposé une méthode de réduction de modèles logiques basée
+ sur l'élimination de composants (sélectionnés manuellement) en
+ réécrivant les fonctions logiques de leurs cibles. L'impact de cette
+ réduction sur le comportement dynamique dans le cadre asynchrone est
+ bien défini, en particulier elle conserve les attracteurs du système
+ complet, mais peut dans certaines conditions en créer de nouveaux ou
+ affecter leur atteignabilité. Après avoir introduit la méthode, je
+ discuterai de critères de sélection des composants à éliminer afin de
+ mieux préserver la dynamique, ainsi que de liens entre cette méthode
+ de réduction et d'autres approches d'analyse statique.<br><br>
+ <b>Loïc Paulevé</b> - <i>Goal-oriented reduction of automata
+ networks</i><br>
+ I'll present on-going results on the reduction of automata networks
+ dedicated to a given reachability goal (e.g., activation of a
+ particular component). Based on previous work on abstract
+ interpretation of traces in automata networks, I'll show that we can
+ identify transitions that are useless for reaching a given goal state.
+ At the end, the reduction produces an automata network that can
+ generate all the minimal traces leading to the given goal, while
+ significantly shrinking its global dynamics.<br><br>
+ <b>Ovidiu Radulescu</b> - <i>Taming the complexity of biochemical
+ networks through model reduction and tropical geometry</i><br>
+ Biochemical networks are used as models of cellular physiology with
+ diverse applications in biology and medicine. In the absence of
+ objective criteria to detect essential features and prune secondary
+ details, networks generated from data are too big and therefore out of
+ the applicability of many mathematical tools for studying their
+ dynamics and behavior under perturbations. However, under
+ circumstances that we can generically denote by multi-scaleness,
+ large biochemical networks can be approximated by smaller and simpler
+ networks. Model reduction is a way to find these simpler models that
+ can be more easily analyzed. We discuss several model reduction
+ methods for biochemical networks with polynomial or rational rate
+ functions and propose as their common denominator the notion of
+ tropical equilibration, meaning finite intersection of tropical
+ varieties in algebraic geometry. Using tropical methods, one can
+ strongly reduce the number of variables and parameters of biochemical
+ network. For multi-scale networks, these reductions are computed
+ symbolically on orders of magnitudes of parameters and variables, and
+ are valid in wide domains of parameter and phase spaces.<br><br>
+ <b>Adrien Richard</b> - <i>Reduction of finite dynamical systems and
+ linear network coding solvability</i><br>
+ Linear network coding transmits data through networks by letting the
+ intermediate nodes combine the messages they receive and forward the
+ combinations towards their destinations. The solvability problem asks
+ whether the demands of all the destinations can be simultaneously
+ satisfied by using linear network coding. This problem can be
+ formulated in terms of fixed points finite dynamical systems, which
+ are usually called discrete networks, or Boolean networks when all the
+ variables are binary variables.<br>
+ Naldi, Remy, Thieffry and Chaouiya (TCS 2011) introduced technics for
+ removing some variables in a finite dynamical system without changing
+ the number of fixed points. In this presentation, we show that this
+ reduction technics can be used to obtain new results on the linear
+ network coding solvability problem.<br>
+ We first show that triangle-free undirected graphs are linearly
+ solvable if and only if they are solvable by routing (this is the
+ first classification result for the linear network coding solvability
+ problem). Then, we exhibit a new class of non-linearly solvable
+ graphs. Finally, we determine large classes of strictly linearly
+ solvable graphs.<br><br>
+ <b>Laurent Tournier</b> - <i>Uncovering regulations in B. subtilis
+ metabolic network, combining optimal resource allocation and
+ Boolean inference</i><br>
+ We previously developed and validated the constraint-based modeling
+ method "Resource Balance Analysis" (RBA) that accurately predicts
+ resource allocation (i.e. growth rate, protein allocation, metabolic
+ configuration) in the model bacterium Bacillus subtilis for a wide
+ range of growth conditions. RBA is able to predict induced/repressed
+ subsystems in the metabolic network, thus mimicking the
+ repression/activation of metabolic pathways by a genetic regulator.
+ The question is now to explore systematically the relation between
+ predicted metabolic configurations and simulated medium composition:
+ (a) to determine a rule of activation of the encoding gene and (b) to
+ determine if the inferred rule coincides with the known biological
+ regulation of the gene. To explore the exponential number of
+ predicted metabolic configurations, we propose to use Boolean
+ inference. In particular, we propose a method to infer monotone
+ (unate) Boolean functions on a minimal support. Applied to the central
+ carbon metabolism of B. subtilis, first results are encouraging as the
+ method predicts most of the regulations as they are known today.
+ </p>
+
+ <h3>Participants</h3>
+ <p align="justify">
+ Wassim ABOU-JAOUDE, Antique, ÉNS<br>
+ Adrien BASSO-BLANDIN, LIP, ÉNS-Lyon<br>
+ Franck DELAPLACE, IBISC, Université d'Évry - Val d'Essonne<br>
+ Damien ÉVEILLARD, LINA, Université de Nantes<br>
+ François FAGES, Lifeware, INRIA Paris-Rocquencourt<br>
+ Éric FANCHON, TIMC-IMAG, CNRS & Université Joseph Fourier de
+ Grenoble<br>
+ Jérôme FERET, Antique, INRIA-Rocquencourt & ÉNS<br>
+ Dan GOREAC, LAMA, Université Paris-Est Marne-la-Vallée<br>
+ Cédric LHOUSSAINE, Cristal, Université Lille 1<br>
+ Guillaume MADELAINE, Cristal, Université Lille 1<br>
+ Tarek MELLITI, IBISC, Université d'Évry - Val d'Essonne<br>
+ Aurélien NALDI, DIMNP, Université Montpellier 2<br>
+ Joachim NIEHREN, Cristal, INRIA-Lille<br>
+ Loïc PAULEVÉ, LRI, CNRS & Université Paris Sud<br>
+ Kévin PERROT, LIF, Aix-Marseille Université<br>
+ Ovidiu RADULESCU, DIMNP, Université de Montpellier<br>
+ Élisabeth REMY, I2M, Aix-Marseille Université<br>
+ Adrien RICHARD, I3S, CNRS & Université de Nice - Sophia Antipolis<br>
+ Sylvain SENÉ, LIF, Aix-Marseille Université<br>
+ Anne SIEGEL, IRISA, CNRS & Université de Rennes<br>
+ Laurent TOURNIER, INRA Jouy en Josas
+ </p>
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
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+
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@@ -0,0 +1,334 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
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+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
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+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
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+ <div class="sidebar_base"></div>
+ </div>
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+
+ <div id="content">
+ <!-- insert the page content here -->
+ <p align="justify">
+ <h1>Séminaire mensuel virtuel (les vendredi à 13h)</h1>
+ <h3>Utiliser ce <a href="https://calendar.google.com/calendar/u/0?cid=aDRvOW11NnBhcnR2OHFpNmtiMDhzN3Z1bGtAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ">lien</a> pour intégrer l'agenda partagé des sémaires Bioss dans votre agenda.</h3><br/><br/>
+ <p/>
+<iframe src="https://calendar.google.com/calendar/embed?height=400&wkst=2&bgcolor=%23ffffff&ctz=Europe%2FParis&src=aDRvOW11NnBhcnR2OHFpNmtiMDhzN3Z1bGtAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ&src=ZnIuZnJlbmNoI2hvbGlkYXlAZ3JvdXAudi5jYWxlbmRhci5nb29nbGUuY29t&color=%234285F4&color=%237986CB&showTitle=0&showTz=0&mode=MONTH&showCalendars=0&showTabs=0&showPrint=0&showNav=1" style="border:solid 1px #777" width="600" height="400" frameborder="0" scrolling="no"></iframe>
+ <p/>
+ <b>Lieu</b> : <a href="https://teams.microsoft.com/l/meetup-join/19%3ameeting_MzY2NThkMzktNjczZS00MTk0LWJhZjgtYjMxNGQ2ZTBlMDdh%40thread.v2/0?context=%7b%22Tid%22%3a%22096815dc-d9eb-4bc3-a5a3-53c77e7d34e2%22%2c%22Oid%22%3a%229f871638-d9f0-4d3c-8f03-d723726606a5%22%7d">URL Teams</a> à utiliser avec l'application Teams ou directement avec Chrome ou Chromium (ne nécessite pas de compte Teams)<br/>
+ <p/>
+ <b><h4>4 juin 2021: <a href="https://www.lri.fr/~speres">Sabine Peres</a> (LISN / Paris Saclay) et
+<a href="https://www.linkedin.com/in/leonard-herault-8a62bb107/?originalSubdomain=fr">Léonard Hérault</a> (Aix Marseille Université)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Sabine Peres</b> (LISN / Paris Saclay) -- <i>Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism.</i> <!-- a href="slides/"></a--><br/>
+
+ Elementary Flux Modes (EFMs) provide a rigorous basis to
+ systematically characterize the steady state, cellular
+ phenotypes, as well as metabolic network robustness and
+ fragility. However, the number of EFMs typically grows
+ exponentially with the size of the metabolic network, leading
+ to excessive computational demands, and unfortunately, a large
+ fraction of these EFMs are not biologically feasible due to
+ system constraints. However, only a few constraints can be
+ integrated in the traditional computation; most of them must
+ be treated in post-processing and thus do not save
+ computational time.
+
+ In this talk, we will present the biological constraints that
+ we integrate into the EFMs calculation. We rely on a hybrid
+ computational tool based on Answer Set Programming (ASP) and
+ Linear Programming (LP) that permits the computation of EFMs
+ while implementing many different types of constraints. We
+ will illustrate this approach to the Escherichia coli core
+ model in considering transcriptional and environmental
+ regulations, thermodynamic constraints, and resource usage
+ considerations. <br/><br/>
+
+ <b>13h30 - 14h00</b> -- <b>Léonard Hérault</b> (Aix Marseille Université) -- <i>Single cell RNA seq assisted synthesis of a boolean transcription factors network to model early hematopoiesis and its alteration with aging.</i><br/>
+
+ We previously characterized early hematopoieisis in young and
+aged mice through hematopoietic stem cell (HSC) sc-RNA-seq
+analysis. Thanks to HSC clustering and pseudotime ordering as well as
+regulon analysis we showed differentiation paths of HSC toward three
+primed states and an accumulation of quiescent HSCs upon
+aging. Starting from these results coupled to current knowledge of
+transcriptionnal regulation of early hematopoiesis we built a boolean
+network using answer set programming in order to model HSC priming and
+its alteration with aging.<br/><br/>
+
+
+ <b><h4>7 mai 2021: <a href="http://www.lsv.fr/~mfuegger/">Matthias Függer</a> (CNRS / ENS Paris-Saclay) et
+<a href="https://www.cristal.univ-lille.fr/profil/ddursoni?lang=fr">Danilo Dursoniah</a> (CRIStAL / Lille)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Matthias Függer</b> (CNRS / ENS Paris-Saclay) -- <i>Distributed Computation with Continual Population Growth.</i> <!-- a href="slides/"></a--><br/>
+ The talk is on recent work towards distributed crcuits within growing bacterial systems.
+ We will focus on majority consensus that plays a key role in tolerating noise within such circuits
+ and discuss the performance and correctness of an algorithm.
+ The talk is based on work with Da-Jung Cho, Corbin Hopper, Manish Kushwaha, Thomas Nowak,
+ and Quentin Soubeyran.
+ <br/><br/>
+
+ <b>13h30 - 14h00</b> -- <b>Danilo Dursoniah</b> (CRIStAL / Lille) -- <i>Limits of a Glucose-Insulin Model to Investigate Intestinal Absorption in Type 2 Diabetes.</i><br/>
+ Abnormal regulation of glucose absorption in the small
+ intestine is an important cause of Type 2 Diabetes (T2D). Even if this
+ hypothesis is clinically well-known, it has not been fundamentally
+ validated yet, mainly due to a lack of reliable metabolic knowledge on
+ the glucose regulation. We aim to test this hypothesis on a highly
+ referenced model composed of ordinary differential equations,
+ published by Dalla Man & al. in 2007. This model is tested by trying
+ to infer reliable parameters from differents original datasets: one
+ featuring the observations of obese diabetic patients, the other from
+ minipigs undergoing several experimental conditions such as intestinal
+ surgery or pancreatectomy. This latter dataset is more reliable than
+ humans due to the relatively low variability between the
+ individuals. In both cases, our work shows the model's limits to
+ predict our post-prandial glycemia and insulinemia time series
+ especially with regard to the crucial complexity of gastro-intestinal
+ regulation.<br/><br/>
+
+ <b><h4>2 avril 2021: <a href="https://annuaire.ifremer.fr/cv/23648">Francis Mairet</a> (Ifremer / Nantes) et <a href="https://www.linkedin.com/in/antrea-pavlou-218733147">Antrea Pavlou</a> (IBIS / Grenoble)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Francis Mairet</b> (Ifremer, Nantes) -- <i>Optimal proteome allocation determines temperature dependence of microbial growth laws.</i> -- <a href="slides/"></a>. <br/>
+ Although the effect of temperature on microbial growth has
+ been widely studied, the role of proteome allocation in bringing
+ about temperature-induced changes remains elusive. In this talk, I
+ will present a coarse-grained model of microbial growth - including
+ the processes of temperature-sensitive protein unfolding and
+ chaperone-assisted (re)folding - that we develop to tackle this
+ problem. We determine the proteome sector allocation that maximizes
+ balanced growth rate as a function of nutrient limitation and
+ temperature. Calibrated with quantitative proteomic data for
+ Escherichia coli, the model allows us to clarify general principles
+ of temperature-dependent proteome allocation and formulate growth
+ laws. The same activation energy for metabolic enzymes and ribosomes
+ leads to an Arrhenius increase in growth rate at constant proteome
+ composition over a large range of temperatures, whereas at extreme
+ temperatures resources are diverted away from growth to
+ chaperone-mediated stress responses. Our approach points at risks and
+ possible remedies for the use of ribosome content to characterize
+ complex ecosystems with temperature variation.<br/><br/>
+
+ <b>13h30 - 14h00</b> -- <b>Antrea Pavlou</b> (IBIS / Grenoble) -- <i>Insights into bacterial resource allocation in dynamically changing environments using a combination of experimental and mathematical approaches..</i><br/>
+ with E. Cinquemani, H. Geiselmann, H. de Jong<br/>
+
+ The relationship between bacterial growth and the environment
+ has been well characterized over the last 50 years. In most studies,
+ however, bacteria are maintained at steady-state growth even though in
+ reality they are rarely in a constant environment. To investigate
+ bacterial adaptation in changing environments, we track growth and
+ gene expression of single- cell bacteria growing in a microfluidic
+ device in changing environments. We examine the behavior of specific
+ ribosomal and metabolic genes in this context using fluorescent
+ protein tags. The experimental results provide a detailed view of
+ resource allocation strategies of bacteria in dynamically changing
+ environments and are helpful in testing the predictions made by
+ resource allocation models of bacterial growth.<br/><br/>
+
+ <b><h4>12 mars 2021: <a href="http://pagesperso.ls2n.fr/~chaffron-s">Samuel Chaffron</a> (Combi team, LS2N, Nantes) et <a href="https://www.lri.fr/~schevalier">Stéphanie Chevalier</a> (LRI, Paris-Saclay)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Samuel Chaffron</b> (Combi team, LS2N, Nantes) -- <i>Environmental vulnerability of the global ocean plankton community interactome.</i> -- <a href="slides/chaffron_samuel.pdf">slides</a>. <br/>
+ Marine plankton form complex communities of interacting
+ organisms at the base of the food web, which sustain oceanic
+ biogeochemical cycles, and help regulate climate. Though
+ global surveys are starting to reveal ecological drivers
+ underlying planktonic community structure, and predicted
+ climate change responses, it is unclear how community-scale
+ species interactions will be affected by climate change. Here
+ we leveraged Tara Oceans sampling to infer a global ocean
+ cross-domain plankton co-occurrence network – the community
+ interactome – and used niche modeling to assess its
+ vulnerabilities to environmental change. Globally, this
+ revealed a plankton interactome self-organized latitudinally
+ into marine biomes (Trades, Westerlies, Polar), and more
+ connected poleward. Integrated niche modeling revealed
+ biome-specific community interactome responses to
+ environmental change, and forecasted most affected lineages
+ for each community. These results provide baseline approaches
+ to assess community structure and organismal interactions
+ under climate scenarios, while identifying plausible plankton
+ bioindicators for ocean monitoring of climate change.<br/><br/>
+
+ <b>13h30 - 14h00</b> -- <b>Stéphanie Chevalier</b> (Lifeware / Inria Saclay) -- <i>Synthesis of Boolean networks from single-cell differentiation data.</i><br/>
+ Processes like cell differentiation and cancerisation have
+ dynamical properties around the notion of trajectory
+ (succession of changes in gene state), non-reachability
+ (bifurcating event) and stability (differentiated
+ cell). Single-cell data on such behaviors are now quite widely
+ available but dynamical modelling with them remains too
+ complex to be commonly leveraged. I will present the approach
+ we develop to automatically infer dynamical models from such
+ data and prior knowledge on gene interactions. The inference
+ method consists in formulating the inference as a Boolean
+ satisfiability problem, described as a logic program
+ containing both the modelling formalism (Most Permissive
+ Boolean network - MPBN) and the data on the biological process
+ (prior knowledge, experimental measurements, dynamics,
+ hypotheses). Several constraints have been implemented in
+ Answer-Set Programming to ensure the desired dynamical
+ properties, and thanks to this logic modeling it is now
+ possible to exhaustively enumerate the MPBN compatible with
+ the constraints of cell differentiation behaviors. In order to
+ leverage single-cell data, I firstly run classification and
+ trajectory reconstruction methods, then data are translated
+ into logical form to describe the cells dynamics. I will
+ present preliminary results obtained for a large-scale
+ modeling of hematopoiesis from cell-scale transcriptomic
+ sequencing data (single-cell RNA-seq data). Potential
+ influences between genes and proteins are extracted from the
+ SIGNOR database, which brings more than 5500 components
+ (genes, proteins and complexes).
+ <br/><br/>
+
+ <h4><b>5 février 2021: <a href="http://www.ens-lyon.fr/LBMC/laboratoire/annuaire/1-gandrillon-olivier">Olivier Gandrillon</a> (LBMC / ENS Lyon) et <a href="http://aurelien.naldi.info/">Aurélien Naldi</a> (Lifeware / Inria Saclay)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Olivier Gandrillon</b> (LBMC / ENS Lyon) -- <i> A probabilistic dynamical framework for Gene Regulatory Network inference and simulation.</i>
+ Joined work with Matteo Bouvier, Alexey Koshkin, Fabien Crauste, Arnaud Bonnaffoux and Olivier Gandrillon.<br/>
+ In this talk, I will first recall our proposal for a GRN model that is simultaneously probabilistic, dynamical, and executable (Herbach et al. 2017; Bonnaffoux et al. 2019). It is specifically designed to reproduce and to predict the time-dependent evolution of the gene expression distributions that we observe at the single-cell level, for example during a differentiation process.<br/>
+I will then address two open questions: (i) How do we compare a model's output to experimental data, that is single-cell-based gene expression distributions? and (ii) How do we compare the output of two different models? We will show that the main difficulty comes from the probabilistic nature of the model: two runs of the same model with the exact same parameter values will generate two different distributions.<br/>
+I will present our current proposal and argue that there is no definitive answer to those questions and that more dedicated research is needed to answer those.<br/>
+Herbach, U., Bonnaffoux, A., Espinasse, T., and Gandrillon, O. (2017). Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC Systems Biology 11, 105.<br/>
+Bonnaffoux, A., Herbach, U., Richard, A., Guillemin, A., Gonin-Giraud, S., Gros, P.-A., and Gandrillon, O. (2019). WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 20, 220.<br/><br/>
+ <b>13h30 - 14h00</b> -- <b>Aurélien Naldi</b> (Lifeware / Inria Saclay) -- <i>Kinetic assumptions in Boolean networks: a case for buffering.</i> -- <a href="slides/2021-02-05_BIOSS_buffered_models_kinetic_assumptions.pdf">slides </a><br/>
+Boolean networks are widely used to study complex biological systems,
+especially in absence of precise kinetic information. Their asynchronous
+interpretation has long been considered as more realistic than the
+synchronous one, as it removes some implicit kinetic assumptions. The
+"most permissive" semantics, which has been recently introduced, removes
+all known remaining assumptions and offers surprisingly good
+computational properties. However, this semantics also enables some
+dynamical behaviors which may conflict with the expected biological
+meaning of many models, in particular it can depend on hidden dual
+interactions between components of the network.
+We propose buffered network as a balance between the implicit kinetic
+assumptions of the asynchronous interpretation and the strong
+generalization of the most permissive semantics. Using this approach to
+refine the results obtained with the most permissive semantics, we
+identified some key analytical results which remain valid when we
+preserve the signs of all interactions, while others should be treated
+more carefully.
+ <br/><br/>
+
+ <b><h4>8 janvier 2021: <a href="https://research-information.bris.ac.uk/en/persons/thomas-e-gorochowski">Thomas E. Gorochowski</a> (University of Bristol, UK) et</b><b> <a href="https://research.pasteur.fr/fr/member/olivier-borkowski">Olivier Borkowski</a> (Inria and Institut Pasteur)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Thomas E. Gorochowski</b> (University of Bristol, UK) -- <i>Using diverse sequencing technologies to accelerate genetic circuit design</i> <br/>
+ <b>Résumé:</b>
+Synthetic genetic circuits are composed of many interconnected parts that must function together in concert to implement desired biological computations. A major challenge when developing new circuits is that genetic parts often display unexpected changes in their performance when used in new ways. Such changes can arise due to contextual effects or unintended interactions with the host cell. In this talk, I will demonstrate how we have been using a variety of sequencing technologies to tackle problem. First, I will show how RNA-sequencing can be used to measure the function of every transcriptional part making up large genetic circuits. This enables us to better understand why some designs fail and helps pinpoint the root cause. Then, I will present some recent work where we combined RNA-sequencing with ribosome profiling and RNA spike-in standards to enable the first large-scale characterization of transcriptional and translational parts in absolute units. Finally, I will discuss some new work that uses long-read nanopore sequencing to enable the characterization of thousands of genetic parts simultaneously to better understand their design constraints. Taken together, the methods presented provide a means for a more complete and quantitative view of the inner workings of genetic circuits and improves our understanding of the rules governing the effective construction of larger and more complex biological systems. <br/><br/>
+ <b>13h30 - 14h00</b> -- <b> Olivier Borkowski</b> (Inria and Institut Pasteur) -- <i>A large-scale exploration of cell-free compositions to maximize protein production using active learning </i><br/>
+ <b>Résumé:</b>
+ Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. We described an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provided a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality. Eventually, we challenged our method with a collection of E. coli cell-free systems using various homemade cell lysates and lysates supplemented with antibiotics to alter the efficiency of transcription and translation processes.<br/>
+Joint work with Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, Paul Soudier, and Jean-Loup Faulon at Génomique Métabolique, Genoscope, and Micalis Institute, INRAE, France
+ <p/>
+
+ <b><h4>6 novembre 2020: <a href="http://www.i3s.unice.fr/~boyenval">Déborah Boyenval</a> (I3S/Sparks) et <a href="https://www.marseille-medical-genetics.org/a-baudot/">Loïc Paulevé</a>(CNRS/LaBRI/Formal Methods)</h4></b>
+ <b>13h00 - 13h30</b> -- <b>Déborah Boyenval</b> (I3S/Sparks) -- <i>Étude des checkpoints du cycle cellulaire : spécification et vérification</i> -- <a href="./slides/Boyenval_gtbioss_6nov20.pdf">slides</a><br/>
+ <b>Résumé:</b>
+Le cycle cellulaire est par définition une succession d'évènements conduisant à la duplication sans erreur de l'ADN (phase S) et l'équitable division d'une cellule mère en deux cellules filles (phase M). Au cours de la progression du cycle cellulaire (G1-S-G2-M), l'intégrité de l'ADN est garantie notamment par les checkpoints. Nos travaux montrent que la modélisation discrète du cycle cellulaire permet de modéliser proprement la notion fondamentale de checkpoint. Un nouveau modèle multivalué du cycle cellulaire est présenté en suivant le formalisme de René Thomas. Le modèle se focalise sur la succession des évènements de régulation qui représente le cycle cellulaire. On y montre que plusieurs permutations de ces évènements sont admissibles, tout en permettant néanmoins de dégager des évènements clefs non permutables qui caractérisent les checkpoints. Cette étude a été rendue possible grâce à l'usage de deux types de méthodes formelles dédiées aux réseaux de régulation multivalués: la logique de Hoare "génétiquement modifiée" et le model-checking pour CTL. L'outil TotemBioNet combine efficacement ces deux approches formelles pour identifier exhaustivement les paramètres dynamiques des modèles compatibles avec nos définitions du cycle cellulaire et de ses checkpoints.</p>
+ <b>13h30 - 14h00</b> -- <b>Loïc Paulevé</b> (CNRS/LaBRI/Formal Methods) -- <i>Most Permissive Boolean Networks in practice</i> -- <a href="./slides/2020-11-06-Pauleve.pdf">slides</a><br/>
+ <b>Résumé:</b>
+ Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. However, (a)synchronous Boolean network, besides being costly to analyze, can preclude the prediction of certain behaviors observed in quantitative systems.<br/>
+ Most Permissive Boolean Networks offer the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.<br/>
+
+In this talk, after an overview of the motivation and properties of MPBNs, I'll focus on their practical usage for the analysis of models of biological networks.<br/>
+Related material:
+<ul>
+<li> <a href="https://www.nature.com/articles/s41467-020-18112-5">https://www.nature.com/articles/s41467-020-18112-5</a></li>
+<li> <a href="https://zenodo.org/record/3936123">https://zenodo.org/record/3936123</a></li>
+</ul>
+ <p/>
+ <b><h4>2 octobre 2020: <a href="https://www6.toulouse.inrae.fr/lipm/Recherche/Pouvoir-pathogene-de-Ralstonia-et-adaptation-a-son-environnement/Membres">Caroline Baroukh</a> (INRA Toulouse) et <a href="https://www.marseille-medical-genetics.org/a-baudot/">Anaïs Baudot</a> (MMG Marseille)</b></h4>
+ <b>13h00 - 13h30</b> -- <b>Caroline Baroukh</b> (INRA Toulouse) -- <i>Modélisation métabolique des interactions plantes-pathogènes</i><br/>
+ <b>Résumé:</b>
+ Les outils de la biologie des systèmes, et plus particulièrement la modélisation métabolique, sont parfaitement adaptés pour étudier l’interaction métabolique hôte-pathogène. En effet, ils permettent de formaliser les systèmes complexes de manière rigoureuse, d’avoir une vision globale et générique, de faire des bilans matières et surtout de faire un lien entre physiologie observée (croissance, excrétion de facteur de virulence, déplétion des substrats) et données génomiques (génome, transcriptome, protéome). Ces approches ont déjà fait leur preuve dans le domaine des biotechnologies et de la biologie de synthèse pour l’optimisation de la production de molécules d’intérêts industriels. Leur adaptation au domaine de la pathologie des plantes peut aider à déchiffrer les stratégies de virulence de pathogènes de plante.<br/>
+
+Après une brève présentation des techniques de modélisation utilisées, deux exemples de l’apport de la modélisation métabolique pour la pathologie des plantes seront présentés. Le premier exemple est la reconstruction et la modélisation semi-automatique des réseaux métaboliques des souches du complexe d’espèces Ralstonia solanacearum, bactéries pathogènes provoquant le flétrissement de nombreuses plantes. L’étude in silico a montré que l’architecture des réseaux métaboliques semble liée à la phylogénie des souches, ainsi qu’au style de vie particulier de certaines souches. Le second exemple est la reconstruction du réseau métabolique de Xylella fastidiosa (souche CFBP8418), phytopathogène bactérien responsables de nombreuses maladies dont l’«Olive Scorch » en Italie. L’étude in silico du métabolisme de cette souche a permis de révéler certaines particularités métaboliques qui impactent fortement la robustesse du pathogène et qui pourrait expliquer en partie sa croissance fastidieuse.
+ </p>
+ <b>13h30 - 14h00</b> -- <b>Anaïs Baudot</b> (MMG Marseille) -- <i>A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks</i><br/>
+ <b>Résumé:</b> The identification of subnetworks of interest - or active modules - by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in multiplex biological networks. MOGAMUN optimizes the scores of the nodes (e.g., their differential expression) and the density of interactions from multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks.
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@@ -0,0 +1,506 @@
+<!DOCTYPE HTML>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html;
+ charset=utf-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
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+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
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+ <li><a href="../../contact.html">Contacts</a></li>
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+
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+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées annuelles du groupe de travail (1ère édition)</h1>
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ Date : 23 novembre 2015
+ </p>
+ <p align="justify">
+ Lieu :
+ Amphithéâtre Turing,
+ Bâtiment Sophie Germain,
+ Université Paris Diderot,
+ Paris.
+ </p>
+ <p align="justify">
+ Organisateurs : <a url="http://www.pps.univ-paris-diderot.fr/~jkrivine/homepage/Home.html">Jean Krivine</a> et <a url="http://www.pps.univ-paris-diderot.fr/~ruet/">Paul Ruet</a>
+ </p>
+
+ <p align="justify">
+ Pour sa première année d'existence, le GT Bioss organise la 1ère
+ édition de ces journées annuelles (qui se tiendront cette année sur
+ une journée). Ces journées se veulent être la rencontre
+ annuelle des membres du groupe de travail "Biologie systémique
+ symbolique", commun au GDR Infomatique mathémétique et au GDR
+ Bio-informatique moléculaire.
+ </p>
+
+
+ <h3>Programme</h3>
+ <p align="left">
+ <b>09h00 - 10h00</b> - <b>Accueil</b><br>
+ <b>10h00 - 11h00</b> - <b>Conférence plénière</b> - Christine Brun -
+ <i>Interactomes of multifunctional proteins</i><br>
+ <b>11h00 - 11h15</b> - <b>Café</b> - <i>Mise en place Session 1
+ exposés courts</i><br>
+ <b>11h15 - 11h30</b> - Célia Biane - <i>Interaction network game
+ applied to drug prediction in precision medicine</i><br>
+ <b>11h30 - 11h45</b> - Sucheendra Palaniappan - <i>Approximating the
+ dynamics of the hybrid stochastic-deterministic apoptosis
+ pathway</i><br>
+ <b>11h45 - 12h00</b> - Adrien Rougny - <i>Two qualitative dynamics
+ semantics for SBGN process description maps</i><br>
+ <b>12h00 - 12h15</b> - François Fages - <i>Synthesizing configurable
+ biochemical implementation of linear systems from their transfer
+ function specifications</i><br>
+ <b>12h15 - 12h30</b> - Nathalie Théret - <i>Microenvironment and
+ activation of TGF-β</i><br>
+ <b>12h30 - 14h00</b> - <b>Déjeuner</b><br>
+ <b>14h00 - 15h00</b> - <b>Conférence plénière</b> - Oded Maler -
+ <i>Dynamical systems biology</i><br>
+ <b>15h00 - 15h15</b> - <b>Café</b> - <i>Mise en place Session 2
+ exposés courts</i><br>
+ <b>15h15 - 15h30</b> - Vincent Danos - <i>Models of growth</i><br>
+ <b>15h30 - 15h45</b> - Jérôme Feret - <i>Une approche algébrique pour
+ détecter et utiliser les symmétries d'un modèle basé sur des règles
+ de récriture</i><br>
+ <b>15h45 - 16h00</b> - Adrien Basso-Blandin - <i>A knowledge
+ representation meta-model for rule-based modelling of signalling
+ networks</i><br>
+ <b>16h00 - 16h15</b> - Loïc Paulevé - <i>Abstractions pour la dynamique
+ des réseaux qualitatifs</i><br>
+ <b>16h15 - 16h30</b> - Paul Ruet - <i>Negative local feedbacks in
+ Boolean networks</i><br>
+ <b>16h30 - 16h45</b> - Adrien Richard - <i>Simple dynamics on
+ graphs</i><br>
+ <b>16h45 - 17h00</b> - <b>Café</b> - <i>Mise en place Session 3 exposés
+ courts</i><br>
+ <b>17h00 - 17h15</b> - Carito Guziolowski - <i>Integrating omics data
+ into large-scale biological networks</i><br>
+ <b>17h15 - 17h30</b> - Gautier Stoll - <i>MaBoSS tool : modeling
+ signaling network in a Boolean framework with continuous time.
+ Principles and applications</i><br>
+ <b>17h30 - 17h45</b> - Vincent Picard - <i>Analyse stationnaire des
+ réseaux de réactions : systèmes de contraintes en modélisation
+ stochastique</i><br>
+ <b>17h45 - 18h00</b> - Virgile Andreani - <i>TBA</i><br>
+ </p>
+
+ <h3>Résumés</h3>
+ <p align="justify">
+ <b>Virgile Andreani</b> - <i>A stochastic model of
+ metabolism and growth</i><br>
+ It has been recently demonstrated that stochastic
+ fluctuations in the expression level of metabolic enzymes
+ can cause growth fluctuations, and that conversely, growth
+ fluctuations can propagate back to perturb gene expression
+ [1]. However, our quantitative understanding of these
+ observations is limited. In particular, the specific
+ contribution to the global phenotypic heterogeneity of these
+ two intertwined processes in unclear. Our objective here is
+ to propose a model that relates in a simple but quantitative
+ manner cell metabolism, gene expression and growth, together
+ with their temporal fluctuations. To do so, we will leverage
+ on and extend the model of Weiße et al. [2] representing in
+ an abstract manner the main aspects of the economy of a
+ growing cell. In this talk I will present our strategy to
+ extend the model.<br>
+ [1] Kiviet et al. Stochasticity of metabolism and growth at
+ the single-cell level, Nature, 2014, 514:376-379.<br>
+ [2] Weiße et al. Mechanistic links between cellular
+ trade-offs, gene expression, and growth, PNAS, 2015,
+ 112(9):E1038-E1047.<br><br>
+ <b>Adrien Basso-Blandin</b> - <i>A knowledge representation
+ meta-model for rule-based modelling of signalling
+ networks</i><br>
+ The study of cellular signalling pathways and their
+ deregulation in disease states, such as cancer, is a large
+ and extremely complex task. Indeed, these systems involve
+ many parts and processes but are studied piecewise and their
+ literatures and data are consequently fragmented,
+ distributed and sometimes - at least apparently -
+ inconsistent. This makes it extremely difficult to build
+ significant explanatory models with the result that effects
+ in these systems that are brought about by many interacting
+ factors are poorly understood. In this context, we introduce
+ a graph-based meta-model, attuned to the representation of
+ cellular signalling networks, which aims to ease this
+ massive cognitive burden on the rule-based curation
+ process. This meta-model is a generalization of that used by
+ Kappa and BNGL which allows for the flexible representation
+ of knowledge at various levels of granularity. In
+ particular, it allows us to deal with information which has
+ either too little, or too much, detail with respect to the
+ strict rule-based meta-model. Our approach provides a basis
+ for the gradual aggregation of fragmented biological
+ knowledge extracted from the literature into an instance of
+ the meta-model from which we can define an automated
+ translation into executable Kappa programs.<br><br>
+ <b>Célia Biane</b> - <i>Interaction network game applied to
+ drug prediction in precision medicine </i><br>
+ Precision medicine aims at the use, in the clinic, of the
+ unique molecular profile of each patient to predict the
+ risks and benefits of treatments. This approach would be
+ particularly helpful in the case of complex diseases such as
+ cancer, where only a fraction of patients are responsive to
+ drugs while others can exhibit severe side-effects. The
+ field is looking forward for new computational methods
+ guiding clinical decision-making toward the best therapy for
+ the patient. In the endeavor of establishing a causal
+ relationship between molecular profiles and clinical
+ phenotypes of patients, network medicine studies the cause
+ of diseases on the molecular interaction networks of
+ patients. In these networks molecules are represented as
+ nodes and interactions between these molecules are
+ represented as edges. In this context, the prediction of
+ therapies results from a decision-making process based on
+ the dynamics of the network. We propose to study the impacts
+ of disease and treatment on the dynamics of molecular
+ networks in order to predict beneficial therapies. We
+ developed a computational model coupling two theoretical
+ frameworks: game theory to model decision-making and Boolean
+ models of dynamics to represent the evolution of the
+ patient's molecular interaction system. We applied the model
+ to best therapeutic strategy prediction in the case of
+ breast cancer.<br><br>
+ <b>Christine Brun</b> - <i>Interactomes of multifunctional
+ proteins (Keynote talk)</i><br><br>
+ <b>Vincent Danos</b> - <i>Models of growth</i><br><br>
+ <b>François Fages</b> - <i>Synthesizing configurable
+ biochemical implementation of linear systems from their
+ transfer function specifications</i><br><br>
+ <b>Jérôme Feret</b> - <i>Une approche algébrique pour
+ détecter et utiliser les symmétries d'un modèle basé sur des
+ règles de récriture</i><br>
+ Nous proposons de décrire des groupes de transformations qui
+ opèrent sur des graphes à sites, et montrons rapidement sous
+ quelles hypothèses ils induisent diverses formes de
+ bisimulations sur diverses sémantiques de Kappa.<br><br>
+ <b>Oded Maler</b> - <i>Dynamical systems biology (Keynote
+ talk)</i><br>
+ In this talk I argue that progress in Biology requires,
+ among other things, a more modern approach to modeling and
+ analysis of dynamical models. Such models should not be
+ restricted to classical dynamical systems but also involve
+ concepts and ideas from discrete-event dynamical systems
+ (automata) and hybrid (discrete-continuous) systems. I will
+ present some recent techniques for exploring the dynamics of
+ under-determined systems, that is, systems that admit
+ uncertainty in initial conditions, parameters and
+ environmental conditions. These techniques, inspired by
+ formal verification, can be used to assess the robustness of
+ proposed models and increase our confidence in their
+ plausibility.<br><br>
+ <b>Sucheendra Palaniappan</b> - <i>Approximating the
+ dynamics of the hybrid stochastic-deterministic apoptosis
+ pathway</i><br>
+ Modeling and analysis of the dynamics of biological systems
+ while accounting for single cell fluctuations is
+ important. In particular, there has been recent work on a
+ hybrid stochastic-deterministic (HSD) model of TRAIL induced
+ apoptosis that combines a deterministic signal transduction
+ modeland a stochastic model for protein turnover that can
+ explain fractional killing and predict the time dependent
+ evolution of cell resistance to TRAIL. While this model is
+ extremely useful for analyzing TRAIL induced apoptosis by
+ drawing simulations in a single cell setting, it can be
+ limiting in cases when we want to analyse the system in a
+ multi-scale setting (say modeling a spheroid of millions of
+ cells at larger time horizon for clinical trials). In such
+ cases, simulating the original model for repeated analysis
+ tasks can become extremely time consuming due to the scale
+ of the resultant system. Instead, one could directly
+ approximate the dynamics of the underlying system as an
+ intermediate level behavioral model and use this
+ approximation instead. In this talk, we will present results
+ describing a minimalist discrete appromixation (Dynamic
+ Bayesian Networks (DBNs) ) of the dynamics of the HSD
+ model. We will describe how analysis tasks on the original
+ HSD model translates to probabilistic inference tasks on the
+ DBN. We will also describe several algorithmic improvements
+ we make over existing analysis methods on DBNs in
+ general.<br><br>
+ <b>Loïc Paulevé</b> - <i>Abstractions pour la dynamique des
+ réseaux qualitatifs</i><br>
+ Un rapide aperçu de résultats et perspectives reposant sur
+ des techniques d'interprétation abstraite pour appréhender
+ la dynamique des réseaux booléens et discrets à grande
+ échelle : réduction et vérification de modèles, prédiction
+ de mutations, reprogrammation cellulaire...<br><br>
+ <b>Vincent Picard</b> - <i>Analyse stationnaire des réseaux
+ de réactions : systèmes de contraintes en modélisation
+ stochastique</i><br>
+ L'étude de la dynamique des réseaux de réactions est un
+ enjeu majeur de la biologie des systèmes. Cela peut être
+ réalisé de deux manières : soit de manière déterministe à
+ l'aide d'équations différentielles, soit de manière
+ probabiliste à l'aide de chaînes de Markov. Dans les deux
+ cas, un problème majeur est celui de la détermination des
+ lois cinétiques impliquées et l'inférence de paramètres
+ cinétiques associés. Pour cette raison, l'étude directe de
+ grands réseaux de réactions est impossible. Dans le cas de
+ la modélisation déterministe, ce problème peut-être
+ contourné à l'aide d'une analyse stationnaire du réseau. Une
+ méthode connue est celle de l'analyse des flux à l'équilibre
+ (FBA) qui permet d'obtenir des systèmes de contraintes
+ linéaires à partir d'informations sur les pentes moyennes
+ des trajectoires. Dans cet exposé je présenterai des pistes
+ pour étendre ces approches dans le contexte stochastique en
+ déduisant des contraintes non nécessairement linéaires à
+ partir d'informations sur les moments (moyennes, variances,
+ covariances) d'un ensemble de trajectoires.<br><br>
+ <b>Adrien Richard</b> - <i>Simple dynamics on graphs</i><br>
+ Biological networks, such gene or neural networks, are often
+ modeled by finite dynamical systems, that is, dynamical
+ systems where each variable evolves in a finite interval of
+ integer A. In this presentation, we address the following
+ question: does the interaction graph of a finite dynamical
+ system can force this system to have a "complex" dynamics ?
+ We provide a negative answer when |A|>2 by proving that, for
+ every signed digraph G, there exists a finite dynamical with
+ interaction graph G that converges toward a unique fixed
+ point in logarithmic time. The boolean case |A|=2 is more
+ difficult, and we provide partial answers instead. For
+ instance, given an unsigned digraph G, we prove that if G
+ contains a directed wheel (resp. is symmetric), there exists
+ a boolean system with interaction graph G that converges
+ toward a unique fixed point in linear time (resp. constant
+ time).<br><br>
+
+ <b>Adrien Rougny</b> - <i>Two qualitative dynamics semantics
+ for SBGN process description maps</i><br>
+ Qualitative dynamics semantics allow to model large reaction
+ networks with unknown kinetic parameters. In this work, we
+ present two qualitative dynamics semantics for reaction
+ networks formalized into the SBGN Process Description
+ language (SBGN-PD). These two semantics, namely the general
+ semantics and the stories semantics, allow to model any
+ SBGN-PD map into an automata network, that can then be
+ simulated to catch the main dynamical features of the
+ network. While the general semantics refines the standard
+ Boolean semantics of reaction networks by taking into
+ account all the main features of SBGN-PD, the stories
+ semantics allows to model several molecules of a network by
+ a unique variable, reducing in this way the size of the
+ models. We present those two semantics and compare them on a
+ large biological network example, the E2F/RB
+ pathway.<br><br>
+
+ <b>Paul Ruet</b> - <i>Negative local feedbacks in Boolean
+ networks</i><br>
+
+ <b>Gautier Stoll</b> - <i>MaBoSS tool: modeling signaling
+ network in a Boolean framework with continuous
+ time. Principles and applications</i><br>
+ MaBoSS is a C++ software, that models signaling network, in
+ a Boolean framework with continuous time. Influences between
+ nodes is given in a specific language, that mixes Boolean
+ logic and real number operators, in order to specify a rate
+ of activation and a rate of inhibition for each node. Each
+ of these rates depends on the Boolean states of the other
+ nodes of the network. MaBoSS applies a continuous time
+ Markov process to a model described in this language, and
+ produces time-dependent probabilities and estimates
+ asymptotic behavior. MaBoSS has been applied to several
+ biological situations (cell cycle, cell fate,
+ senescence/geroconversion). Quantitative modeling results
+ can be confronted to experimental data, resulting in
+ interesting interpretations. MaBoSS modeling framework can
+ be interpreted as a method between ODE and Boolean
+ modeling.<br><br>
+
+ <b>Nathalie Théret</b> - <i>Microenvironment and activation
+ of TGF-β</i><br>
+
+ Transforming growth factor TGF-β plays pivotal roles in
+ numerous biological processes including tissue homeostasis
+ and morphogenesis, and is implicated in a number of
+ pathological processes including inflammation, fibrosis and
+ cancer. Targeting the deleterious effects of TGF-β without
+ affecting its physiological role is the common goal of
+ therapeutic strategies. While several strategies based on
+ blocking TGF-β antibodies or small inhibitors of TGF-β
+ receptors have been investigated, the impact of the cellular
+ microenvironment that triggers and regulates TGF-β
+ bioavailability has not been taken into account so
+ far. Indeed, TGF-β is synthesized in large amount and exists
+ as an inactive molecule, latent TGF-β (LAP-TGF-β), which
+ needs to be activated and released from the extracellular
+ matrix network. Changes in the cellular microenvironment in
+ pathological situations are expected to play a direct and
+ important role in the alteration of TGF-β activity. As a
+ result, the complexity of microenvironment networks requires
+ modeling approaches to understand and predict how TGF-β
+ activation is regulated and ultimately identify putative
+ targets suitable for future therapy. To model the dynamic of
+ TGF-β activation out of the cell, we use a rule-based
+ modeling approach (Kappa language), which consists in
+ describing explicitly the biochemical structure of chemical
+ species as graphs of connected proteins. Rewriting rules
+ encoding complexation, decomplexation, and
+ post-translational modifications are well suited for
+ describing the extracellular matrix network that regulates
+ TGF-β activation. Literature curation (116 publications from
+ 1988 to 2014) allowed us to collect information relative to
+ the regulation of TGF-β activation in the extracellular
+ matrix and to elaborate a model integrating 31 proteins and
+ 96 rules. Using proteomic data to parameterize the model, we
+ investigated the sensitivity of TGF-β release to changes in
+ microenvironment. Such program will provide a significant
+ input in our understanding of the dynamics of TGF-β
+ activation regulated by microenvironment. We believe that
+ the extracellular microenvironment is a major parameter to
+ consider in future therapeutic approaches targeting TGF-β in
+ cancer.
+ </p>
+
+ <h3>Participants</h3>
+ <p align="justify">
+ Patrick AMAR, LRI, Université Paris Sud<br>
+ Virgile ANDREANI, ENS<br>
+ Paolo Ballarini, MICS, École centrale Paris<br>
+ Adrien BASSO-BLANDIN, LIP, ENS-Lyon<br>
+ Éléonore BELLOT, ENS<br>
+ Célia BIANE, IBISC, Université d'Évry - Val d'Essonne<br>
+ Marc BOUFFARD, LRI, Université Paris Sud<br>
+ François BOULIER, CRISTAL, Université de Lille<br>
+ Christine BRUN, TAGC, CNRS Marseille<br>
+ Vincent DANOS, DIENS, CNRS Paris<br>
+ Victorien DELANNÉE, IRISA, Université de Rennes<br>
+ Franck DELAPLACE, IBISC, Université d'Évry - Val d'Essonne<br>
+ Cinzia DI GIUSTO, I3S, Université de Nice - Sophia Antipolis<br>
+ Mohamed ELATI, ISSB, Université d'Évry - Val d'Essonne<br>
+ François FAGES, INRIA Saclay<br>
+ Éric FANCHON, TIMC-IMAG, CNRS Grenoble<br>
+ Jérôme FERET, INRIA Paris<br>
+ Enrico FORMENTI, I3S, Université de Nice - Sophia Antipolis<br>
+ Christine FROIDEVAUX, LRI, Université Paris Sud<br>
+ Olivier GANDRILLON, LBMC, CNRS Lyon<br>
+ Carito GUZIOLOWSKI, IRCCyN, École centrale de Nantes<br>
+ Adrien HUSSON<br>
+ Jean KRIVINE, PPS, CNRS Paris<br>
+ Jonathan LAURENT, ENS<br>
+ Pascale LE GALL, MICS, École centrale Paris<br>
+ Cédric LHOUSSAINE, CRISTAL, Université de Lille<br>
+ Guillaume MADELAINE, CRISTAL, Université de Lille<br>
+ Morgan MAGNIN, IRCCyN, École centrale de Nantes<br>
+ Oded Maler, VERIMAG, CNRS Grenoble<br>
+ Tarek MELLITI, IBISC, Université d'Évry - Val d'Essonne<br>
+ Joachim NIEHREN, INRIA Lille<br>
+ Sucheendra PALANIAPPAN, IRISA, INRIA Rennes<br>
+ Loïc PAULEVÉ, LRI, CNRS Orsay<br>
+ Kévin PERROT, LIF, Université d'Aix-Marseille<br>
+ Vincent PICARD, LINA, Université de Nantes<br>
+ Damien REGNAULT, IBISC, Université d'Évry - Val d'Essonne<br>
+ Élisabeth REMY, I2M, CNRS Marseille<br>
+ Adrien RICHARD, I3S, CNRS Nice - Sophia Antipolis<br>
+ Adrien ROUGNY, LRI, Université Paris Sud<br>
+ Olivier ROUX, IRCCyN, École centrale de Nantes<br>
+ Paul RUET, PPS, CNRS Paris<br>
+ Sylvain SENÉ, LIF, Université d'Aix-Marseille<br>
+ Anne SIEGEL, IRISA, CNRS Rennes<br>
+ Pierre SIEGEL, LIF, Université d'Aix-Marseille<br>
+ Gautier STOLL, Institut Curie Paris<br>
+ Guillaume TERRADOT, DIENS, ENS<br>
+ Nathalie THÉRET, IRISA, INSERM Rennes<br>
+ Serghei VERLAN, LACL, Université Paris Est Créteil<br>
+ </p>
+
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
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+
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+\usepackage{tabularx}
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+\fancyfoot{}
+
+
+\begin{document}
+\vspace*{4mm}
+\centerline{\Large \bf Journées annuelles du GT Bioss}\bigskip
+\centerline{(\large \bf GDR IM -- GDR BIM)}
+\centerline{\large \bf 1er et 2 juillet 2016}\bigskip
+
+\centerline{
+ \begin{tabular}{p{.3\textwidth}p{.3\textwidth}p{.3\textwidth}}
+ & \hrulefill &
+ \end{tabular}
+}\medskip\medskip
+
+\section*{Participants}\medskip\medskip\medskip
+
+\centerline{
+ \begin{tabular}{|p{.3\textwidth}|p{.7\textwidth}|l|}
+ \hline
+ Qui? & D'où? & Là?\\
+ \hline\hline
+ Jalouli ACHREF & Univ. Limoges & \\\hline
+ Émilie ALLART & CRISTAL, Univ. Lille & \\\hline
+ Adel Amar AMOURI & Dpt. de bio., Université d'Oran & \\\hline
+ Emna BEN ABDALLAH & IRCCyN, EC Nantes & \\\hline
+ Adrien BASSO-BLANDIN & LIP, ENS-Lyon & \\\hline
+ Grégory BATT & Lifeware, INRIA Saclay & \\\hline
+ Guillaume BEAUMONT & IPS2, Univ. Paris Sud & \\\hline
+ Emmanuelle BECKER & IRSET, Univ. Rennes & \\\hline
+ Hugues BERRY & Beagle, INRIA Lyon & \\\hline
+ Arnaud BONNAFFOUX & LBMC, ENS-Lyon & \\\hline
+ Ferdinanda CAMPORESI & Dpt. d'info., ENS & \\\hline
+ Thomas COKELAER & Biomics, Inst. Pasteur & \\\hline
+ Victorien DELANNÉE & IRISA, Univ. Rennes & \\\hline
+ Ronan DUCHESNE & LBMC, ENS-Lyon & \\\hline
+ Maxime FOLSCHETTE & I3S, Univ. Nice-Sophia & \\\hline
+ Enrico FORMENTI & I3S, Univ. Nice-Sophia & \\\hline
+ Olivier GANDRILLON & LBMC, CNRS Lyon & \\\hline
+ Nils GIORDANO & IBIS, INRIA Grenoble & \\\hline
+ Dan GOREAC & LAMA, Univ. Marne-la-Vallée & \\\hline
+ Carito GUZIOLOWSKI & IRCCyN, EC Nantes & \\\hline
+ Pierre GUILLON & I2M, CNRS Marseille & \\\hline
+ Russ HARMER & LIP, ENS-Lyon & \\\hline
+ Ulysse HERBACH & LBMC, ENS-Lyon & \\\hline
+ Marcelline KAUFMAN & Dpt. de bio. théor., Univ. Bruxelles & \\\hline
+ Cédric LHOUSSAINE & CRISTAL, Univ. Lille & \\\hline
+ Guillaume MADELAINE & CRISTAL, Univ. de Lille & \\\hline
+ Bertrand MIANNAY & IRCCyN, EC Nantes & \\\hline
+ Jean-Michel MULLER & LIP, CNRS Lyon & \\\hline
+ Loïc PAULEVÉ & LRI, CNRS Orsay & \\\hline
+ Kévin PERROT & LIF, Univ. Aix-Marseille & \\\hline
+ Sylvain PRIGENT & Sysbio, Univ. Chalmers & \\\hline
+ Élisabeth REMY & I2M, CNRS Marseille & \\\hline
+ Adrien RICHARD & I3S, CNRS Nice-Sophia & \\\hline
+ Marie-France SAGOT & ERABLE, INRIA Lyon & \\\hline
+ Nicolas SCHABANEL & IRIF, CNRS Paris & \\\hline
+ Sylvain SENÉ & LIF, Univ. Aix-Marseille & \\\hline
+ Anne SIEGEL & IRISA, CNRS Rennes & \\\hline
+ Laurent TRILLING & TIMC-IMAG, Univ. Grenoble & \\\hline
+ Jean-Yves TROSSET & BIRL, SupBioTech & \\\hline
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\ \hline
+ \end{tabular}
+}
+
+\pagebreak
+
+\centerline{
+ \begin{tabular}{|p{.3\textwidth}|p{.7\textwidth}|l|}
+ \hline
+ Qui? & D'où? & Là?\\
+ \hline\hline
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
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+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\ \hline
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\ \hline
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
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+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\ \hline
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+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\ \hline
+ \textcolor{White}{a} & \textcolor{White}{a} & \textcolor{White}{a}\\
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+
+\pagebreak
+
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+ \end{tabular}
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+
+\pagebreak
+
+\centerline{
+ \begin{tabular}{|p{.3\textwidth}|p{.7\textwidth}|l|}
+ \hline
+ Qui? & D'où? & Là?\\
+ \hline\hline
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diff --git a/manif/jnbioss_201607/jnbioss201607.html b/manif/jnbioss_201607/jnbioss201607.html
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+++ b/manif/jnbioss_201607/jnbioss201607.html
@@ -0,0 +1,679 @@
+<!DOCTYPE HTML>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html;
+ charset=utf-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
+
+<body>
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected
+ page - to highlight which page you're on -->
+ <li><a href="../../index.html">Accueil</a></li>
+ <li><a href="../../membres.html">Membres</a></li>
+ <li><a href="../../manif.html">Manifestations</a></li>
+ <li><a href="../../projets.html">Projets</a></li>
+ <li><a href="../../contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="content_header"></div>
+
+ <div id="site_content">
+
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées annuelles du groupe de travail (2ème édition)</h1>
+
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ Date : 1er et 2 juillet 2016
+ </p>
+ <p align="justify">
+ Lieu :
+ Salle "conférence",
+ 1, place de l'École (en rez de chaussée, en dessous de la Maison
+ des mathématiques et de l'informatique, en face de l'amphithéâtre
+ Charles Mérieux),
+ 69007 Lyon<br>
+ Plan d'accès <a href="https://www.google.fr/maps/place/1+Place+de+l'École,+69007+Lyon-7E-Arrondissement/@45.729955,4.8257707,17z/data=!3m1!4b1!4m2!3m1!1s0x47f4ea2beb0a4f0b:0x6b747c560d0e395f">ici</a>
+ </p>
+
+ <p align="justify">
+ Organisateurs :
+ <a url="http://www.ens-lyon.fr/LBMC/spip/spip.php?article262">Olivier
+ Gandrillon</a>,
+ <a url="http://www.cristal.univ-lille.fr/~lhoussai/">Cédric
+ Lhoussaine</a>,
+ <a url="http://iml.univ-mrs.fr/~remy/remy.html">Élisabeth
+ Remy</a>,
+ <a url="http://pageperso.lif.univ-mrs.fr/~sylvain.sene/">Sylvain
+ Sené</a> et
+ <a url="http://www.irisa.fr/dyliss/anne.siegel">Anne
+ Siegel</a>
+ </p>
+
+ <p align="justify">
+ La deuxième édition des journées annuelles du GT Bioss va
+ se dérouler à la suite des Journées ouvertes de biologie,
+ informatique et mathématiques
+ (<a href="http://jobim2016.sciencesconf.org">JOBIM</a>)
+ organisées par la Société française de bio-informatique
+ (<a href="http://www.sfbi.fr">SFBI</a>). Ainsi, les 1er et 2
+ juillet 2016, les membres du GT auront le plaisir de se rencontrer
+ autour de conférences autour des thèmes suivants :<br>
+ - la modélisation stochastique en biologie ;<br>
+ - la régulation génétique ;<br>
+ - le métabolisme.
+ </p>
+
+ <h3>Inscription</h3>
+ <p align="justify">
+ L'inscription, gratuite mais obligatoire, se fait en
+ remplissant le formulaire
+ accessible <a href="https://docs.google.com/forms/d/1cs20k_5aeBQK8gFkhNmgh9uSIjmDuRkKZqnWMcrYCwk/viewform?c=0&w=1&usp=mail_form_link">ici</a>.
+ </p>
+
+ <h3>Orateurs invités</h3>
+ <p align="justify">
+ Grégory BATT, INRIA Saclay<br>
+ Marcelline KAUFMAN, Université libre de Bruxelles<br>
+ Marie-France SAGOT, INRIA Lyon<br>
+ </p>
+
+ <h3>Programme</h3>
+
+ <h4>Vendredi 1er juillet</h4>
+
+ <p align="left">
+ <b>09h00 - 09h30</b> - <b>Accueil</b><br>
+
+ <b>09h30 - 09h45</b> - <b>Introduction des journées</b><br>
+
+ <b>09h45 - 10h30</b> - <b>Conférence plénière</b> - Grégory Batt -
+ <i>Predicting long-term effects of apoptosis-inducing drug
+ treatments: coupling signal transduction pathways with
+ stochastic protein turnover models </i><br>
+
+ <b>10h30 - 11h00</b> - Bertrand Miannay -
+ <i>Identification des voies de signalisation impliquées dans
+ le myélome multiple par programmation par contrainte</i><br>
+ <b>11h00 - 11h30</b> - Arnaud Bonnaffoux -
+ <i>Toward a dynamic multi-scale/level approach for gene regulatory
+ network inference</i><br>
+ <b>11h30 - 12h00</b> - Nicolas Schabanel -
+ <i>Folding Turing is hard but feasible</i><br>
+
+ <b>12h00 - 13h30</b> - <b>Pause déjeuner</b><br>
+
+ <b>13h30 - 14h15</b> - <b>Conférence plénière</b> -
+ Marie-France Sagot -
+ <i>Species interactions from a metabolism perspective</i><br>
+
+ <b>14h15 - 14h45</b> - Nils Giordano -
+ <i>Dynamical allocation of cellular resources as an optimal
+ control problem</i><br>
+ <b>14h45 - 15h15</b> - Victorien Delannée -
+ <i>A modeling approach to evaluate the balance between
+ bioactivation and detoxification of MeIQx in human
+ hepatocytes</i><br>
+
+ <b>15h15 - 15h45</b> - <b>Discussion Bioss / GDR</b><br>
+
+ <b>15h45 - 16h15</b> - <b>Pause</b><br>
+
+ <b>16h15 - 16h45</b> - Hugues Berry -
+ <i>Estimating the effects of spatial non-homogeneities in
+ intracellular diffusion-reactions</i><br>
+ <b>16h45 - 17h15</b> - Dan Goreac -
+ <i>Hybrid designing using stochastic backward
+ equations</i><br>
+ <b>17h15 - 17h45</b> - Guillaume Madelaine -
+ <i>Structural simplifications of reaction networks: the
+ confluence problem</i><br>
+ <b>17h45 - 18h15</b> - Ferdinanda Camporesi -
+ <i>Context-sensitive flow analyses: a hierarchy of model
+ reductions</i><br>
+ </p>
+
+
+ <h4>Samedi 2 juillet</h4>
+
+ <p align="left">
+
+ <b>09h00 - 09h45</b> - <b>Conférence plénière</b> -
+ Marcelline Kaufman -
+ <i>On multistationarity in chemical reaction networks</i><br>
+
+ <b>09h45 - 10h15</b> - Kévin Perrot -
+ <i>On the flora of asynchronous locally non-monotonic
+ Boolean networks</i><br>
+ <b>10h15 - 10h45</b> - Élisabeth Remy -
+ <i>Discrete dynamics of compound regulatory circuits</i><br>
+
+ <b>10h45 - 11h00</b> - <b>Pause</b><br>
+
+ <b>11h00 - 11h30</b> - Loïc Paulevé -
+ <i>Around reachability in automata networks</i><br>
+
+ <b>11h30 - 12h00</b> - Emna Ben Abdallah -
+ <i>Inference of biological regulatory networks from time
+ series data</i><br>
+
+ <b>12h00 - 12h30</b> - Adrien Richard -
+ <i>Points fixes dans les réseaux booléens monotones</i><br>
+ </p>
+
+ <h3>Résumés</h3>
+ <p align="justify">
+ <b>Grégory Batt</b> - <i>Predicting long-term effects of
+ apoptosis-inducing drug treatments: coupling signal
+ transduction pathways with stochastic protein turnover
+ models</i><br>
+ TRAIL is an anti-cancer drug that induces apoptosis
+ selectively in cancer cells. Unfortunately even high doses
+ of TRAIL do not kill all cells and subsequent TRAIL
+ treatments are transiently less effective. Despite extensive
+ studies, a mechanistic understanding of these phenomena is
+ still lacking. In this talk, I will present an extension of
+ a previously-proposed model describing TRAIL signal
+ transduction in Hela cells (Spencer et al, Nature 2011) with
+ models accounting for the turnover of the proteins involved
+ in the pathway at the cell level and the dynamics (growth
+ and death) of the cell population in monolayers or in 3D
+ spheroids. This model is minimalistic in the sense that it
+ uses default values from the literature for all but two
+ parameters. Yet, it explains the existence of survivors
+ (fractional killing), the increased resistance of the
+ surviving population and its transient aspect. The analysis
+ of model predictions calls into question the importance of
+ survival pathways and highlights the critical role of the
+ stochastic turnover of proteins in zymogen-based pathways in
+ which activated forms are rapidly degraded.<br><br>
+
+ <b>Emna Ben Abdallah</b> - <i>Inference of biological
+ regulatory networks from time series data</i><br>
+ With the development of high-throughput data, there is a
+ growing need for methods that automatically generate
+ (resp. revise) admissible models. Our research aims at
+ providing a logical approach to infer Biological Regulatory
+ Networks based on given time series data. We propose a new
+ methodology for models expressed through a timed extension
+ of the Process Hitting framework (well suited for biological
+ systems). The main purpose is to have as a result the most
+ consistent network as possible with the observed data. The
+ originality of our work relies on the integration of
+ quantitative time delays directly in our learning
+ approach.<br>
+ Taking as input a background knowledge under the form of
+ influence graph and time series data, the contribution of
+ our method lies in the fact that we identify the set of
+ actions between biological components by concretizing the
+ signs (negative or positive) besides providing thresholds
+ and associating the quantitative time delays. Starting from
+ the structure of the system and its experimental time
+ series, the method addresses both inference and revision:
+ (1) If no previous dynamic model is given, we infer the
+ dynamics of the system. (2) Otherwise we take profit from
+ new time series to revise actions and delays.<br>
+ We will show the benefits of such automatic approach on
+ dynamical biological model, the circadian clock, and we
+ conduct benchmarks on the DREAM4 datasets, a popular
+ reverse-engineering challenge, in order to discuss the
+ computational performances of our algorithm.<br><br>
+
+ <b>Hugues Berry</b> - <i>Estimating the effects of spatial
+ non-homogeneities in intracellular
+ diffusion-reactions</i><br>
+
+ The inner of living cells exhibits disorder, non-homogeneity
+ and obstruction. For instance, cell membranes are
+ heterogeneous collections of hierarchical spatial domains
+ with various length scales and timescales (e.g., fences,
+ lipid rafts, and caveolae) that spatially modulate the
+ diffusion of proteins. This defines a spatially
+ nonhomogeneous diffusion problem with position-dependent
+ diffusion coefficient. The impact of these deviations from
+ simple Brownian motion on the biochemical reactions that
+ take place in cells cannot be studied with the classical
+ mass-action laws of biochemical kinetics and are just
+ starting to be explored by spatially-explicit stochastic
+ simulations. In this talk, I will present an overview of the
+ recent modelling work carried out in our group on the
+ effects of receptor clustering on the dynamics of
+ ligand-binding equilibrium, and on correlations in gene
+ positions for repressilator-like gene regulation loops. Our
+ results suggest that spatial non-homogeneities are potent
+ modulators of the apparent affinity of the equilibrium
+ reaction or of the dynamical regime itself, even when the
+ elementary reaction rates are not altered.<br><br>
+
+ <b>Arnaud Bonnaffoux</b> - <i>Toward a dynamic
+ multi-scale/level approach for gene regulatory network
+ inference</i><br>
+
+ Gene regulatory networks (GRN) play an important role in
+ many biological processes, such as differentiation, and
+ their identification has raised great expectations for
+ understanding cell behavior. Many computational GRN
+ inference approaches have been described, which are based on
+ expression data but they face common issues such as data
+ scarcity, high dimensionality or population blurring (Chai
+ et al., 2014). We believe that recent high-throughput single
+ cell expression data (see e.g. Pina et al., 2012 ; Shalek et
+ al., 2014) acquired in time-series will allow to overcome
+ these issues and give access to causality, instead of
+ « simple » correlation, for gene interactions. Causality is
+ very important for mechanistic model inference and
+ biological relevance because it enables the emergence of
+ cellular decision-making. Emergent properties of a
+ mechanistic model of a GRN should then match with
+ multi-scale (molecular/cellular) and multi-level (single
+ cell/population) observations. We will expose a GRN
+ inference framework based on these assumptions. It follows
+ three steps:<br>
+ 1. Node parametric inference. We have inferred
+ the parameters from a stochastic mechanistic model of gene
+ expression, the Random Telegraph model (Kim and Marioni,
+ 2013), thank's to time-series single cell expression data
+ from a population of chicken erythrocyte progenitor during
+ their differentiation process (Gandrillon et al., 1999)<br>
+ 2. Model reduction. This is mostly an ongoing work, and will
+ make use of specific constraints applying to the network.<br>
+ 3. The final step will consist in network inference
+ constrained by dynamic multi-scale/level
+ observations.<br><br>
+
+ <b>Ferdinanda Camporesi</b> - <i>Context-sensitive flow
+ analyses: a hierarchy of model reductions</i><br>
+ Rule-based modelling allows very compact descriptions of
+ protein-protein interaction networks. However, combinatorial
+ complexity increases again when one attempts to describe
+ formally the behaviour of the networks, which motivates the
+ use of abstractions to make these models more
+ coarse-grained. Context-insensitive abstractions of the
+ intrinsic flow of information among the sites of chemical
+ complexes through the rules have been proposed to infer
+ sound coarse-graining, providing an efficient way to find
+ macro-variables and the corresponding reduced models. In
+ this paper, we propose a framework to allow the tuning of
+ the context-sensitivity of the information flow analyses and
+ show how these finer analyses can be used to find fewer
+ macro-variables and smaller reduced differential
+ models.<br><br>
+
+
+ <b>Victorien Delannée</b> - <i>A modeling approach to
+ evaluate the balance between bioactivation and
+ detoxification of MeIQx in human hepatocytes</i><br>
+
+ Heterocyclic aromatic amines (HAA) are environmental and
+ food contaminants that are potentially carcinogen for
+ human. 2-Amino-3-methylimidazo(4,5-f)-quinoxaline (MeIQx) is
+ one of the most abundant HAA formed in cooked meat. MeIQx is
+ metabolized by cytochrome P450 1A2 in human liver into
+ detoxification and bioactivation products. Once
+ bioactivated, MeIQx metabolites can lead to DNA adduct
+ formation responsible for further genome instability. Using
+ a computational approach, we develop a numerical model for
+ MeIQx metabolism that predicts the MeIQx biotransformation
+ into detoxification or bioactivation pathways according to
+ the concentration of MeIQx. Our model permits to
+ investigate the balance between bioactivation (i.e. DNA
+ adduct formation pathway through Ester-O-NH-MeIQx) and
+ detoxification of MeIQx in order to predict the behaviour of
+ this environmental contaminant in human liver.<br>
+ Our results demonstrate that 1) the detoxification pathway
+ predominates, 2) predicting the bioactivation and the
+ detoxification for any initial concentration of MeIQx at any
+ time is feasible for any dataset and 3) the ratio between
+ detoxification and bioactivation pathways is not linear and
+ shows a maximum at 10µM of MeIQx in hepatocyte cell
+ model.<br><br>
+
+ <b>Nils Giordano</b> - <i>Dynamical allocation of cellular
+ resources as an optimal control problem: novel insights
+ into microbial growth strategies</i><br>
+ Microbial physiology exhibits growth laws that relate the
+ macromolecular composition of the cell to the growth
+ rate. Recent work has shown that these empirical
+ regularities can be derived from coarse-grained models of
+ resource allocation. While these studies focus on
+ steady-state growth, such conditions are rarely found in
+ natural habitats, where microorganisms are continually
+ challenged by environmental fluctuations. The aim of this
+ paper is to extend the study of microbial growth strategies
+ to dynamical environments, using a self-replicator model. We
+ formulate dynamical growth maximization as an optimal
+ control problem that can be solved using Pontryagin’s
+ Maximum Principle. We compare this theoretical gold standard
+ with different possible implementations of growth control in
+ bacterial cells. We find that simple control strategies
+ enabling growth-rate maximization at steady state are
+ suboptimal for transitions from one growth regime to
+ another, for example when shifting bacterial cells to a
+ medium supporting a higher growth rate. A near-optimal
+ control strategy in dynamical conditions is shown to require
+ information on several, rather than a single physiological
+ variable. Interestingly, this strategy has structural
+ analogies with the regulation of ribosomal protein synthesis
+ by ppGpp in the enterobacterium Escherichia coli. It
+ involves sensing a mismatch between precursor and ribosome
+ concentrations, as well as the adjustment of ribosome
+ synthesis in a switch-like manner. Our results show how the
+ capability of regulatory systems to integrate information
+ about several physiological variables is critical for
+ optimizing growth in a changing environment.<br><br>
+
+ <b>Dan Goreac</b> - <i>Hybrid designing using stochastic
+ backward equations</i><br>
+ We present some targeted-behaviour based issues in the
+ hybrid modelling of networks. The common method is derived
+ from the theory of BSDEs (backward stochastic differential
+ equations) by interpreting the reaction speeds as externally
+ controlled (thus, modifiable) parameters. In the case of
+ first-order (linear) models, we give explicit (algebraic)
+ conditions on the sets of parameters leading to
+ "controllability" (targeted behaviour). For more general
+ systems, if the time allows it, we give an intuition on how
+ parameters might be chosen by using reflected backward
+ equations and embedding in spaces of measures.<br><br>
+
+ <b>Marcelline Kaufman</b> - <i>On multistationarity in
+ chemical reaction networks</i><br>
+ Résumé au format
+ pdf <a href="res/res_Kaufman.pdf">ici</a>.<br><br>
+
+ <b>Guillaume Madelaine</b> - <i>Structural simplifications
+ of reaction networks: the confluence problem</i><br>
+ Models in system biology are often big, and need to be
+ simplified in order to be analyzed, simulated or
+ verified. We will first present a set of simplification
+ rules for reaction networks without kinetic rates. This
+ simplification preserves the non-deterministic semantics, in
+ terms of reachability of final strongly connected
+ components, called attractors. Then we will extend the
+ reaction networks with kinetic rates. We will show that,
+ under partial steady-state assumptions, we can simplify the
+ networks by removing some linear intermediate molecular
+ species, while preserving the deterministic semantics of the
+ other species. We will focus on the confluence of this
+ simplification, that is do we always obtain the same fully
+ simplified network, independently of the order in which the
+ simplification rules are applied. We will show that removing
+ the linear intermediate species is not confluent in
+ general. By adding another rule that simplifies some
+ "dependent reactions", we will show that we can always
+ obtain the same structure of the network and the same
+ ODEs. However, the distribution of the kinetic rates between
+ the reactions can be different. <br><br>
+
+ <b>Bertrand Miannay</b> - <i>Identification des voies de
+ signalisation impliquées dans le myélome multiple par
+ programmation par contrainte</i><br>
+ Résumé au format
+ pdf <a href="res/res_Miannay.pdf">ici</a>.<br><br>
+
+ <b>Loïc Paulevé</b> - <i>Around reachability in automata
+ networks</i><br>
+ Many elaborated questions in systems biology involve the one
+ of reachability : the existence / inevitability of a
+ sequence of events leading from a state to another. Some
+ involve the verification of reachability, many more the
+ inference of mutations for its control. Reachability is a
+ difficult computational problem: it is PSPACE-complete for
+ Automata Networks / Petri nets with finite discrete state
+ space. Methods relying on network topology, concurrency,
+ abstract interpretation, model reduction, aim at coping with
+ reachability in large scale networks. In this talk, I'll
+ give an overview of a range of these methods and related
+ tools, with their applications to model-checking, dynamical
+ bifurcation identification, control target prediction, and
+ cellular differentiation.<br><br>
+
+ <b>Kévin Perrot</b> - <i>On the flora of asynchronous
+ locally non-monotonic Boolean networks</i><br>
+ Studies on the dynamics of Boolean networks (BNs) have
+ mainly focused on monotonic networks, where fundamental
+ questions on the links relating their static and dynamical
+ properties have been raised and addressed. In this
+ presentation, we will explore analogous questions on
+ non-monotonic networks, xor-BNs, that are BNs where all the
+ local transition functions are xor-functions. Using
+ algorithmic tools, we will present a general
+ characterisation of the asynchronous dynamics for most of
+ the cactus xor-BNs and strongly connected xor-BNs, through
+ new bisimulation equivalences specific to xor-BANs.<br><br>
+
+ <b>Élisabeth Remy</b> - <i>Discrete dynamics of compound
+ regulatory circuits</i><br>
+ In biological regulatory networks represented in terms of
+ signed, directed graphs, topological motifs such as circuits
+ are known to play key dynamical roles. We present results on
+ the dynamical impact of the addition of a short-cut in a
+ regulatory circuit. More precisely, based on a Boolean
+ formalisation of regulatory graphs, we have unrolled
+ complete descriptions of the discrete dynamics of particular
+ motifs, under the synchronous and asynchronous updating
+ schemes. These motifs are made of a circuit of arbitrary
+ length, combining positive and negative interactions in any
+ sequence, encompassing a short circuit, and using AND, OR
+ and XOR logical rules.<br><br>
+
+ <b>Adrien Richard</b> - <i>Points fixes dans les réseaux
+ booléens monotones</i><br>
+ Les réseaux booléens sont des systèmes dynamiques où chaque
+ variable ne peut prendre que deux états possibles: 0 ou
+ 1. Depuis les travaux pionniers de Kauffman et Thomas, ce
+ sont des modèles très classiques pour les réseaux de
+ gènes. Dans ce contexte, les points fixes sont d'un intérêt
+ particulier: ils correspondent à des patterns stables
+ d'expression des gènes souvent reliés à des fonctions
+ cellulaires bien précises. Cependant, les premières
+ informations disponibles sur un réseau de gènes concernent
+ généralement le graphe d'interaction du réseau et non sa
+ dynamique.<br>
+ Une question naturelle est donc la suivante:
+ que peut-on dire sur les points fixes d'un réseau booléen en
+ fonction de son graphe d'interaction seulement ?<br>
+ Dans cette exposé, on présente une étude du plus grand
+ nombre de points fixes qu'un réseau booléen monotone peut
+ admettre en fonction de son graphe d'interaction. On donnera
+ des bornes inférieures et supérieures qui ne dépendent que
+ de la structure des cycles du graphe d'interaction. Les deux
+ paramètres centraux seront, d'une part, la taille d'un plus
+ petit ensemble de sommets intersectant tous les cycles et,
+ d'autre part, le plus grand nombre de cycles
+ disjoints. L'étude fera intervenir des théorèmes, classiques
+ en combinatoire, sur le treillis booléen et ses antichaines.<br>
+ C'est un travail réalisé en collaboration avec Julio Aracena
+ et Lilian Salinas (Université de Concepcion, Chili)
+ disponible à l'adresse suivante:
+ <a href="http://arxiv.org/abs/1602.03109">
+ http://arxiv.org/abs/1602.03109</a>.<br><br>
+
+ <b>Marie-France Sagot</b> - <i>Species interactions from a
+ metabolism perspective</i><br>
+
+ The frontier between different species may be considered
+ very fuzzy as is more and more observed. Organisms are no
+ longer perceived as single genetically identical individuals
+ and are rather considered as part of communities. At its
+ extreme, one could see thus the whole of life as forming one
+ single community, or a community of communities interacting
+ sometimes closely and for long periods of evolutionary
+ time. Such interactions appear essential to understand some
+ if not all of the most fundamental evolutionary and
+ functional questions related to living organisms. They
+ however remain very little explored by computational
+ biologists, perhaps due to the difficult modelling and
+ computational issues raised. Yet, because of the complexity
+ and singularity of these communities, it is clear that
+ experimental data alone do not allow to fully understand the
+ global capacities and functions of these organisms and their
+ interactions. In this talk, I will briefly present some of
+ the models and algorithms, in the case related to
+ metabolism, that we have recently been developing with the
+ goal of better understanding some such close and often
+ persistent interactions. I will also mention a much longer
+ term objective of this work that would be to become able in
+ some cases to suggest the means of controlling for
+ equilibrium in an interacting community.<br><br>
+
+ <b>Nicolas Schabanel</b> - <i>Folding Turing is hard but
+ feasible</i><br>
+ We introduce and study the computational power of Oritatami,
+ a theoretical model to explore greedy molecular folding, by
+ which the molecule begins to fold before waiting the end of
+ its production. This model is inspired by our recent
+ experimental work demonstrating the construction of shapes
+ at the nanoscale by folding an RNA molecule during its
+ transcription from an engineered sequence of synthetic
+ DNA. While predicting the most likely conformation is known
+ to be NP-complete in other models, Oritatami sequences fold
+ optimally in linear time. Although our model uses only a
+ small subset of the mechanisms known to be involved in
+ molecular folding, we show that it is capable of efficient
+ universal computation, implying that any extension of this
+ model will have this property as well.<br>
+ We develop several general design techniques for programming
+ these molecules. Our main result in this direction is an
+ algorithm in time linear in the sequence length, that finds
+ a rule for folding the sequence deterministically into a
+ prescribed set of shapes depending of its environment. This
+ shows the corresponding problem is fixed-parameter tractable
+ although we proved it is NP-complete in the number of
+ possible environments. This algorithm was used effectively
+ to design several key steps of our constructions.<br>
+ </p>
+
+ <h3>Participants</h3>
+ <p align="justify">
+ Jalouli ACHREF, Université de Limoges<br>
+ Vicente ACUNA, CMM, Santiago, Chili<br>
+ Émilie ALLART, CRISTAL, Université de Lille<br>
+ Adel Amar AMOURI, Dpt. de biologie, Université d'Oran<br>
+ Emna BEN ABDALLAH, IRCCyN, École centrale de Nantes<br>
+ Adrien BASSO-BLANDIN, LIP, ENS-Lyon<br>
+ Grégory BATT, Lifeware, INRIA Saclay<br>
+ Guillaume BEAUMONT, IPS2, Université Paris Sud<br>
+ Emmanuelle BECKER, IRSET, Université de Rennes<br>
+ Hugues BERRY, Beagle, INRIA Lyon<br>
+ Arnaud BONNAFFOUX, LBMC, ENS-Lyon<br>
+ Ferdinanda CAMPORESI, DIENS, ENS<br>
+ Thomas COKELAER, Biomics, Institut Pasteur<br>
+ Victorien DELANNÉE, IRISA, Université de Rennes<br>
+ Ronan DUCHESNE, LBMC, ENS-Lyon<br>
+ Maxime FOLSCHETTE, I3S, Université de Nice - Sophia Antipolis<br>
+ Enrico FORMENTI, I3S, Université de Nice - Sophia Antipolis<br>
+ Olivier GANDRILLON, LBMC, CNRS Lyon<br>
+ Nils GIORDANO, INRIA Grenoble<br>
+ Dan GOREAC, LAMA, Université Paris-Est Marne-la-Vallée<br>
+ Carito GUZIOLOWSKI, IRCCyN, École centrale de Nantes<br>
+ Pierre GUILLON, I2M, CNRS Marseille<br>
+ Russ HARMER, LIP, ENS-Lyon<br>
+ Ulysse HERBACH, LBMC, ENS-Lyon<br>
+ Marcelline KAUFMAN, Dpt. de chimie physique et biologie théorique,
+ Université libre de Bruxelles<br>
+ Cédric LHOUSSAINE, CRISTAL, Université de Lille<br>
+ Guillaume MADELAINE, CRISTAL, Université de Lille<br>
+ Bertrand MIANNAY, IRCCyN, École centrale de Nantes<br>
+ Jean-Michel MULLER, LIP, CNRS Lyon<br>
+ Loïc PAULEVÉ, LRI, CNRS Orsay<br>
+ Kévin PERROT, LIF, Université d'Aix-Marseille<br>
+ Arnaud PORET, LBMC, ÉNS-Lyon<br>
+ Sylvain PRIGENT, Sysbio, Université de Chalmers<br>
+ Élisabeth REMY, I2M, CNRS Marseille<br>
+ Adrien RICHARD, I3S, CNRS Nice - Sophia Antipolis<br>
+ Marie-France SAGOT, ERABLE, INRIA Lyon<br>
+ Nicolas SCHABANEL, IRIF, CNRS Paris<br>
+ Sylvain SENÉ, LIF, Université d'Aix-Marseille<br>
+ Anne SIEGEL, IRISA, CNRS Rennes<br>
+ Laurent TRILLING, TIMC-IMAG, Université de Grenoble<br>
+ Jean-Yves TROSSET, BIRL, SupBioTech
+ </p>
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
+ </div>
+
+ <div id="content_footer"></div>
+
+ <div id="footer">
+ <p><a href="../../index.html">Accueil</a>
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+ | <a href="../../manif.html">Manifestations</a>
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+ | <a href="../../contact.html">Contact</a></p>
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+\fancyfoot{}
+
+
+\begin{document}
+\vspace*{4mm}
+\centerline{\Large \bf Journées annuelles du GT Bioss}\bigskip
+\centerline{(\large \bf GDR IM -- GDR BIM)}
+\centerline{\large \bf 1er et 2 juillet 2016}\bigskip
+
+\centerline{
+ \begin{tabular}{p{.3\textwidth}p{.3\textwidth}p{.3\textwidth}}
+ & \hrulefill &
+ \end{tabular}
+}\medskip\medskip
+
+\section*{Programme}\medskip\medskip\medskip
+
+\subsection*{\textcolor{DarkGreen}{Vendredi 1er juillet}}\medskip
+
+\begin{tabular}{p{.15\textwidth}p{.85\textwidth}}
+ 09h00 - 09h30 & \textbf{Accueil}\\[2mm]
+ 09h30 - 09h45 & \textbf{Introduction des journées}\\[2mm]
+ 09h45 - 10h30 & \textbf{Grégory Batt} -- \emph{\textbf{Predicting long-term effects of
+ apoptosis-inducing drug treatments: coupling signal transduction pathways with
+ stochastic protein turnover models}}\\[2mm]
+ 10h30 - 11h00 & Bertrand Miannay -- Identification des voies de signalisation impliquées
+ dans le myélome multiple par programmation par contrainte\\[1mm]
+ 11h00 - 11h30 & Arnaud Bonnaffoux -- Toward a dynamic multi-scale/level approach for
+ gene regulatory network inference\\[1mm]
+ 11h30 - 12h00 & Nicolas Schabanel -- Folding Turing is hard but feasible\\[2mm]
+ 12h00 - 13h30 & \textbf{Pause déjeuner}\\[2mm]
+ 13h30 - 14h15 & \textbf{Marie-France Sagot} -- \emph{\textbf{Species interactions from a
+ metabolism perspective}}\\[2mm]
+ 14h15 - 14h45 & Nils Giordano -- Dynamical allocation of cellular resources as an optimal
+ control problem\\[1mm]
+ 14h45 - 15h15 & Victorien Delannée -- A modeling approach to evaluate the balance
+ between bioactivation and detoxification of MeIQx in human hepatocytes\\[2mm]
+ 15h15 - 15h45 & \textbf{Discussion Bioss / GDR}\\[2mm]
+ 15h45 - 16h15 & \textbf{Pause}\\[2mm]
+ 16h15 - 16h45 & Hugues Berry -- Estimating the effects of spatial non-homogeneities in
+ intracellular diffusion-reactions\\[1mm]
+ 16h45 - 17h15 & Dan Goreac -- Hybrid designing using stochastic
+ backward equations\\[1mm]
+ 17h15 - 17h45 & Guillaume Madelaine -- Structural simplifications of reaction networks: the
+ confluence problem\\[1mm]
+ 17h45 - 18h15 & Ferdinanda Camporesi -- Context-sensitive flow analyses: a hierarchy of
+ model reductions
+\end{tabular}\medskip
+
+\subsection*{\textcolor{DarkGreen}{Samedi 2 juillet}}\medskip\medskip
+
+\begin{tabular}{p{.15\textwidth}p{.85\textwidth}}
+ 09h00 - 09h45 & \textbf{Marcelline Kaufman} -- \emph{\textbf{On multistationarity in
+ chemical reaction networks}}\\[2mm]
+ 09h45 - 10h15 & Kévin Perrot -- On the flora of asynchronous locally non-monotonic
+ Boolean networks\\[1mm]
+ 10h15 - 10h45 & Élisabeth Remy -- Discrete dynamics of compound regulatory
+ circuits\\[2mm]
+ 10h45 - 11h00 & \textbf{Pause}\\[2mm]
+ 11h00 - 11h30 & Loïc Paulevé -- Around reachability in automata networks\\[1mm]
+ 11h30 - 12h00 & Emna Ben Abdallah -- Inference of biological regulatory networks from
+ time series data\\[1mm]
+ 12h00 - 12h30 & Adrien Richard -- Points fixes dans les réseaux booléens monotones
+\end{tabular}
+
+\pagebreak
+
+\vspace*{4mm}\section*{Participants inscrits}\medskip\medskip
+
+\centerline{
+ \begin{tabular}{llll}
+ Jalouli & ACHREF & & Université de Limoges\\
+ Émilie & ALLART & CRISTAL & Université de Lille\\
+ Adel Amar & AMOURI & Dpt. de biologie & Université d'Oran\\
+ Emna & BEN ABDALLAH & IRCCyN & École centrale de Nantes\\
+ Adrien & BASSO-BLANDIN & LIP & ENS-Lyon\\
+ Grégory & BATT & Lifeware & INRIA Saclay\\
+ Guillaume & BEAUMONT & IPS2 & Université Paris Sud\\
+ Emmanuelle & BECKER & IRSET & Université de Rennes\\
+ Hugues & BERRY & Beagle & INRIA Lyon\\
+ Arnaud & BONNAFFOUX & LBMC & ENS-Lyon\\
+ Ferdinanda & CAMPORESI & DIENS & ENS\\
+ Thomas & COKELAER & Biomics & Institut Pasteur\\
+ Victorien & DELANNÉE & IRISA & Université de Rennes\\
+ Ronan & DUCHESNE & LBMC & ENS-Lyon\\
+ Maxime & FOLSCHETTE & I3S & Université de Nice - Sophia Antipolis\\
+ Enrico & FORMENTI & I3S & Université de Nice - Sophia Antipolis\\
+ Olivier & GANDRILLON & LBMC & CNRS Lyon\\
+ Nils & GIORDANO & IBIS & INRIA Grenoble\\
+ Dan & GOREAC & LAMA & Université Paris-Est Marne-la-Vallée\\
+ Carito & GUZIOLOWSKI & IRCCyN & École centrale de Nantes\\
+ Pierre & GUILLON & I2M & CNRS Marseille\\
+ Russ & HARMER & LIP & ENS-Lyon\\
+ Ulysse & HERBACH & LBMC & ENS-Lyon\\
+ Marcelline & KAUFMAN & Dpt. de chimie physique & Université libre de Bruxelles\\
+ & & \hspace*{6.3mm}et biologie théorique & \\
+ Cédric & LHOUSSAINE & CRISTAL & Université de Lille\\
+ Guillaume & MADELAINE & CRISTAL & Université de Lille\\
+ Bertrand & MIANNAY & IRCCyN & École centrale de Nantes\\
+ Jean-Michel & MULLER & LIP & CNRS Lyon\\
+ Loïc & PAULEVÉ & LRI & CNRS Orsay\\
+ Kévin & PERROT & LIF & Université d'Aix-Marseille\\
+ Sylvain & PRIGENT & Sysbio & Université de Chalmers\\
+ Élisabeth & REMY & I2M & CNRS Marseille\\
+ Adrien & RICHARD & I3S & CNRS Nice - Sophia Antipolis\\
+ Marie-France & SAGOT & ERABLE & INRIA Lyon\\
+ Nicolas & SCHABANEL & IRIF & CNRS Paris\\
+ Sylvain & SENÉ & LIF & Université d'Aix-Marseille\\
+ Anne & SIEGEL & IRISA & CNRS Rennes\\
+ Laurent & TRILLING & TIMC-IMAG & Université de Grenoble\\
+ Jean-Yves & TROSSET & BIRL & SupBioTech
+ \end{tabular}
+}
+
+\end{document}
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@@ -0,0 +1,836 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
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+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
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+ <li><a href="../../manif.html">Manifestations</a></li>
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+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées annuelles du groupe de travail (3ème édition)</h1>
+
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ Date : Lundi 13 mars (toute la journée) et mardi 14 mars (matin) 2017
+ </p>
+ <p align="justify">
+ Lieu : Amphitheatre de l'espace colloque du CNRS, 1919 route de Mende à Montpellier.
+ </p>
+
+ <p align="justify">
+ Organisateurs : Ovidiu Radulescu, Grégory Batt, Cédric Lhoussaine, Elisabeth Remy et Anne Siegel
+ </p>
+
+
+<p align="justify">
+ La troisième édition des journées annuelles du GT Bioss va
+ se dérouler juste avant les journées nationales du GDR
+Informatique-Mathématique. Ainsi, les 13 et 14 mars 2017, les membres du
+ GT auront le plaisir de se rencontrer
+ autour de conférences autour des thèmes suivants :</p>
+<div class="">- la modélisation des systèmes biologiques et ses applications;</div>
+
+<div class="">- les systèmes dynamiques discrets, hybrides;</div>
+
+<div class="">- les langages de modélisation et leurs sémantiques (déterministes, non-déterministes, stochastiques);</div>
+
+<div class="">- la vérification de modèles;</div>
+
+<div class="">- la réduction de modèles et leur pouvoir prédictif (sous incertitude);</div>
+
+<div class="">- l'inférence d’interactions et de règles à partir de données biologiques;</div>
+
+<div class="">- et plus généralement tout problème de modélisation lié à l’intégration de données réelles. </div>
+
+<div class=""><br class="">
+</div>
+<p align="justify"></p>
+
+
+ <h3>Inscription</h3>
+ <p align="justify">
+ L'inscription, gratuite mais obligatoire, se fait en
+ remplissant le formulaire
+ accessible <a href="https://docs.google.com/forms/d/e/1FAIpQLSe9ppo0cu9LLTPrXTaw0dpHYzUNyACkeXst15Ag5ZX9jhoQ5A/viewform?c=0&w=1">ici.</a>
+ </p>
+
+ <h3>Orateurs invités</h3>
+
+<p align="justify">
+ <meta charset="utf-8"><span style="font-size:14.666666666666666px;font-family:Arial;color:#000000;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;"
+ id="docs-internal-guid-34c540d9-cafa-f741-0321-10cf7fc90e08"></span>
+ Anaïs Baudot, Institut de Mathématiques de Marseille. <br>
+ Jakob Ruess, INRIA Saclay.<br>
+ Thomas Sturm, CNRS-LORIA, Nancy & Max Planck Institute, Saarbrücken, Germany.<br>
+ </p>
+
+
+ <h3>Programme</h3>
+
+ <h4>Lundi 13 Mars</h4>
+ <p align="left">
+ <b>09h00 - 09h25</b> - <b>Accueil - Café</b><br>
+ <i>Chairwoman: Anne Siegel</i><br>
+ <b>09h25 - 09h30</b> - Ovidiu Radulescu - <i>Introduction</i>.<br>
+ <b>09h30 - 10h15</b> - <b>Conférencier invité</b> - Jakob Ruess
+ - <i>Control of bio-digital systems in single cells.</i><br>
+ <b>10h20 - 10h35</b> - Ovidiu Radulescu -
+ <i>Time dependent multivariate distributions for
+ piecewise-deterministic models of gene networks.</i> <a href="slides/Radulescu.pdf">slides</a><br>
+ <b>10h40 - 10h55</b> - Stefano Casagranda - <i>Principal
+ Process Analysis and reduction of biological models with order of
+ magnitude.</i><br> <a href="slides/Casagranda.pdf">slides</a>
+
+ <b>11h00 - 11h30</b> - <b>Pause</b><br>
+ <i>Chairman: Cédric Lhoussaine</i><br>
+ <b>11h30 - 11h45</b> - Yves-Stan Le Cornec - <i>Le Projet Kami.</i>
+ <br>
+ <b>11h50 - 12h05</b> - Andreea Beica - <i>Synchronous balanced
+ analysis.</i> <a href="slides/Beica.pdf">slides</a>i<br>
+
+ <b>12h10 - 12h25</b> - François Fages - <i>Complexité
+ algorithmique des calculs analogiques et compilation de fonctions
+ mathématiques en réactions biochimiques élémentaires.</i><br><br>
+
+ <b>12h30- 13h45 - Pause déjeuner</b><br><br>
+ <i>Chairman: Ovidiu Radulescu</i><br>
+ <b>13h45 - 14h30</b> - <b>Conférencier invité</b> - Thomas
+ Sturm - <i>Symbolic Methods in Bifurcation Analysis.</i> <a href="slides/Sturm.pdf">slides</a><br>
+ <b>14h35 - 14h50</b> - Matthieu Pichene - <i>Predicting tumor growth
+ using a statistical layered population abstraction.</i> <a href="slides/Pichene.pdf">slides</a><br>
+ <b>14h55 - 15h10</b> - François Boulier - <i>Identifiabilité, équations
+ intégro-différentielles et neurobiologie.</i> <a href="slides/Boulier.pdf">slides</a><br>
+
+ <b>15h15 - 15h40</b> - <b>Pause</b><br>
+ <i>Chairman: François Fages</i><br>
+ <b>15h40 - 15h55</b> - Clémence Frioux - <i>Hybrid gap-filling to
+ reconcile qualitative and quantitative abstractions of
+ metabolism.</i><br>
+ <b>16h00 - 16h15</b> - Emilie Allart - <i>Elementary modes refine abstract
+ interpretation of reaction networks with partial kinetic
+ information.</i><br>
+ <b>16h20 - 16h35</b>; Marie Beurton-Aimar - <i>How to display
+ patterns inside elementary flux modes.</i><br>
+
+ <b>16h40 - 17h00</b> - <b>Pause</b><br>
+ <i>Chairman: Grégory Batt</i><br>
+ <b>17h00 - 17h15</b> - Florian Bridoux - <i>On The Cost Of Simulating A
+ Parallel Boolean Automata Networks By A Sequential One.</i><br>
+ <b>17h20 - 17h35</b> - Aurélien Naldi - <i>Reversed logical models for the study of
+ basins of attraction.</i><br>
+ <b>17h40 - 17h55</b> - Pierre Siegel - <i>Des logiques non-monotones aux
+ systèmes dynamiques discrets (SDD).</i><br>
+ <b>18h00 - 18h15</b> - Laurent Trilling - <i>Apport de la non
+ monotonie pour la modélisation logique de réseuax de régulation
+ génique.</i><br><br>
+
+ <b>19h15</b> - Repas au
+ Restaurant <a href="http://www.trinquefougasse.com/o-nord">Trinque
+ Fougasse O'Nord</a> 1581 route de Mende (à 350m à pied du lieu de la
+ conférence), formule 30€, vin et concert jazz compris. Il est encore
+ possible de s’inscrire au repas!
+ </p>
+
+
+ <h4>Mardi 14 Mars</h4>
+ <p align="left">
+ <i>Chairwoman: Élisabeth Rémy</i><br>
+ <b>09h00 - 09h45</b>: <b>Conférencière invitée</b> - Anais Baudot
+ - <i>Mining and modeling biological networks to study rare and common human diseases.</i><br>
+
+ <b>09h50 - 10h10</b>: <b>Pause</b><br>
+
+ <b>10h10 - 10h25</b>: Jean Coquet - <i>Analysis of TGF-β signaling networks to find
+ different families of trajectories.</i><br>
+ <b>10h30 - 10h45</b>: Arnaud Poret - <i>Linking
+ Cancer Models with Therapeutic Effects.</i><br>
+ <b>10h50 - 11h05</b>: Amos Korman - <i>Confidence sharing: an economic
+ strategy for efficient information flows in animal
+ groups.</i><br>
+
+ <b>11h10 - 11h30</b>: <b>Pause</b><br>
+ <i>Chairman: Laurent Trilling</i><br>
+ <b>11h30 - 11h45</b>: Ofer Feinerman - <i>Algorithmic
+ challenges in ant cooperative transport.</i><br>
+ <b>11h50 - 12h05</b>: Jonathan Behaegel - <i>Réseaux génétiques hybrides: de
+ la logique de Hoare à l'identification de paramètres.</i><br>
+ <b>12h10 - 12h25</b>: Celia Biane - <i>Inférence d'action sur les réseaux
+ pour la reprogrammation cellulaire.</i><br>
+ </p>
+ <h3>Résumés</h3>
+ <p align="justify">
+
+ <b>Jakob Ruess (INRIA Saclay)</b> - <i> Control of bio-digital systems
+ in single cells.</i><br> Akin to the developments in industry, also
+ in scientific wet labs more and more manual tasks are being
+ automated and many experiments are nowadays carried out by computer
+ controlled robots. Such experiments are, however, still almost
+ exclusively pre-planned by the scientist as a series of exact
+ instructions that are to be carried out by the robot. Industry, on
+ the other hand, has seen developments such as self-driving cars
+ where the user specifies a goal and the system decides for itself
+ how to best attain that goal. In this talk, I will present our
+ recent efforts to construct autonomous bio-digital systems that are
+ based on real-time computer-to-single cell communication. In
+ particular, I will present an experimental/technological platform in
+ which cells, equipped with an optogenetically inducible promoter,
+ signal to a computer through microscopy. The computer processes the
+ incoming data in real time and sends back light signals to the
+ cells. Experiments on this platform can therefore be specified as
+ algorithms that map the list of received signals from all the cells
+ into a list of optogenetic stimulations. To demonstrate the
+ functionality of this setup, I will show results obtained with two
+ different algorithms implemented on the platform. First, we used a
+ model-predictive controller to drive the expression of a gene coding
+ for a fluorescent reporter protein and managed to ensure that all
+ cells follow the same gene expression target profile despite
+ significant heterogeneity of the cells and even in the presence of
+ environmental perturbations. Subsequently, we used the same
+ controller to make all cells follow different target profiles,
+ demonstrating that the platform can be used to structure populations
+ into pre-definable dynamic gene expression phenotypes. Finally, I
+ will provide results where we coupled an algorithm mimicking a
+ negative feedback loop and a digital cell-to-cell communication
+ mechanism to the gene in order to produce synchronized fluorescence
+ oscillations in the cells.<br><br>
+
+ <b>Ovidiu Radulescu (University of Montpellier, DIMNP lab)</b> -
+ <i>Time dependent multivariate distributions for
+ piecewise-deterministic models of gene networks.</i><br> We discuss
+ piecewise-deterministic approximations of gene networks dynamics. These
+ approximations capture in a simple way the stochasticity of gene expression and
+ the propagation of expression noise in networks and circuits. By using partial
+ omega expansions, piecewise deterministic approximations can be formally derived
+ from the more commonly used Markov pure jump processes. We are interested in
+ time dependent multivariate distributions that describe the stochastic dynamics
+ of the gene networks. This problem is difficult even in the simplified
+ framework of piecewise deterministic processes. We consider three methods to
+ compute these distributions: the direct Monte Carlo simulation, the numerical
+ integration of the Liouville-master equation and the push-forward method. We
+ present applications of this approach to stochastic biological switches and
+ logical gates that can be used as memories and computing elements in synthetic
+ biocomputing machines.<br><br>
+
+ <b>Stefano Casagranda (INRIA Sophia-Antipolis, BICOLORE group)</b> -
+ <i>Principal Process Analysis and reduction of biological models with
+ order of magnitude.</i><br> We present a simple method that allows to
+ analyze the biological processes of a dynamical model and classify
+ them. Along the system trajectories, we decompose the model into
+ biological meaningful processes and then study their activity or
+ inactivity during the time evolution of the system. The structure of
+ the model is then reduced to the core mechanisms involving only the
+ active processes. The initial conditions are supposed to lie in some
+ rectangle, that could represent one order of magnitude for the
+ variables. Keeping only the active processes, we obtain the principal
+ processes in the rectangle and then in the adjacent rectangles where
+ the trajectories may have a transition. Finally we obtain a partition
+ of the space with a reduced model within each rectangle. We apply
+ these techniques to a classical model of gene expression with protein
+ and messenger RNA.<br><br>
+
+ <b>Yves-Stan Le Cornec (ENS Lyon)</b> <i>Le Projet Kami.</i><br>
+ Rule-based modelling enables one to describe complex biological
+ systems by defining and combining together low level
+ mechanisms. However, the details of these mechanisms and the exact
+ conditions for each interaction are not always well known and often
+ spread apart across multiple articles which impedes the process of
+ building a model. KAMI is a software application as well as a
+ graph-based formalism which aims to aggregate biological knowledge in
+ an incremental and flexible way as well as ensuring its consistency.
+ In KAMI, basic pieces of knowledge are represented as graphs called
+ nuggets which are ideally all "typed" by another graph (the action
+ graph). This has the effect of organizing and combining the knowledge
+ contained inside the nuggets as well as providing a graphical overview
+ of it. This action graph is itself typed by a meta-model which
+ provides syntactic restrictions and structure to the model. This in
+ particular facilitates the use of well defined graph transformations
+ when needed, for instance to merge together multiple knowledge bases
+ which can use different formats, to communicate with other existing
+ tools, or to make the meta-model itself evolve if needed.<br> In
+ practice, we developed a generic graph rewriting library in python
+ (Regraph) and a web interface (RegraphGui) that are used to build and
+ manipulate the biological knowledge base. Rule-based models written in
+ kappa can then be generate and sent to the KaSim simulator.<br><br>
+
+ <b>Andrea Beica (ENS, Paris)</b> - <i>Synchronous balanced
+ analysis.</i><br> When modeling Chemical Reaction Networks, a commonly
+ used mathematical formalism is that of Petri Nets, with the usual
+ inter-leaving execution semantics. We aim to substitute to a Chemical
+ Reaction Network, especially a growth one (i.e., for which an
+ exponential stationary phase exists), a piecewise synchronous
+ approximation of the dynamics: a resource-allocation-centered Petri
+ Net with maximal-step execution semantics. In the case of unimolecular
+ chemical reactions, we prove the correctness of our method and show
+ that it can be used either as an approximation of the dynamics, or as
+ a method of constraining the reaction rate constants (an alternative
+ to flux balance analysis, using an emergent formally de ned notion of
+ growth rate as the objective function), or a technique of refuting
+ models.<br><br>
+
+ <b>François Fages (Inria Saclay Ile-de-France, Lifeware group)</b> - <i>Complexité
+ algorithmique des calculs analogiques et compilation de fonctions
+ mathématiques en réactions biochimiques élémentaires.</i><br> L’aspect
+ continu de beaucoup d’interactions protéiques nous conduit à
+ considérer des modes de calcul mixtes analogiques-digitaux, pour
+ lesquels des résultats récents de Bournez, Graca et Pouly en théorie
+ de la calculabilité et de la complexité analogiques établissent des
+ liens fondamentaux avec la programmation classique. Nous dérivons de
+ ces résultats un compilateur de spécifications comportementales en
+ systèmes de réactions biochimiques que l’on peut comparer aux circuits
+ naturels résultats de l’évolution. Nous illustrons cette démarche par
+ l’exemple du module de signalisation MAPK qui a une fonction de
+ convertisseur analogique-digital dans la cellule, et par le contrôle
+ du cycle cellulaire.<br><br>
+
+ <b>Thomas Sturm (CNRS-LORIA, Nancy & Max Planck Institute,
+ Saarbrücken, Germany)</b> - <i>Symbolic Methods in Bifurcation
+ Analysis.</i><br> We are going to discuss the potential of symbolic
+ computation methods with the analysis of biological networks for
+ bifurcations. Those methods include various real quantifier
+ elimination methods in combination with preprocessing heuristics,
+ real triangularizations, and subtropical approaches. Applications
+ range from the identification of Hopf bifurcations in comparatively
+ small models to work-in-progress towards the identification of
+ saddelnode bifurcations with respect to more than one parameter in a
+ model of MAPK from the BioModels database. After several years of
+ systematic work and collaborations in the area we feel confident
+ that there is a potential for symbolic tools in biological research,
+ in particular with parametric situations, although there are obvious
+ obstacles: Firstly, our approaches are not fully automatic yet, so
+ that users have to understand their theoretical background to some
+ extent. Secondly, for complexity reasons the methods cannot be
+ expected to scale to all relevant problem sizes. It is thus
+ important to identify application areas that are sufficiently large
+ to justify the effort for both developers and potential users. We
+ hope to stimulate critical but open-minded constructive discussions
+ around those topics.<br><br>
+
+ <b>Matthieu Pichene (Inria Rennes, SUMO group)</b> - <i>Predicting
+ tumor growth using a statistical layered population
+ abstraction.</i><br> Tumor growth models such as B. Waclaw et
+ al. (2015) can be a useful tool to design protocols for cancer
+ treatment. A challenging problem is that models (usually cell based)
+ can rapidly be time and memory consuming due to tumors reaching
+ hundreds of thousands of cells. These cells can each duplicate, move
+ and die. We designed an abstraction that represent the tumor
+ evolution. Tumor is partitioned into layers. In each layer, few data
+ are kept, e.g. the density of the layer, but not the individual cell
+ data. From statistics drawn from agent based models such as
+ TumourSimulator B. Waclaw et al. (2015) for small tumors, we
+ automatically obtain a statistical model that can reproduce the tumor
+ evolution, and predict the evolution of bigger tumor under
+ treatment. Compared to the original model, we can reproduce very
+ faithfully the evolution of the tumor. Also, we have clear speed up
+ (e.g. 30 times for a tumor of 100.000 cells).<br><br>
+
+ <b>François Boulier (University of Lille, CRIStAL lab)</b>
+ - <i>Identifiabilité, équations intégro-différentielles et
+ neurobiologie.</i><br> L'identifiabilité est une étude théorique d'un
+ modèle mathématique destinée à établir si les paramètres du modèle ont
+ une valeur complètement déterminée par les mesures
+ expérimentales. C'est une étude de principe. Dans le cas d'un modèle
+ présenté sous la forme d'un système d'équations différentielles
+ paramétriques non linéaires, cette propriété peut être établie, après
+ une étape de calcul formel, par l'analyse du « polynôme entrée-sortie
+ » du modèle. Le résultat de l'étude peut ensuite être réemployé pour
+ effectuer une estimation de paramètres à partir de données
+ expérimentales réelles. Des travaux récents en calcul formel et en
+ théorie du contrôle ouvrent de nouvelles perspectives théoriques et
+ pratiques visant à généraliser cette approche en faisant apparaître
+ des équations intégro-différentielles. Dans cet exposé, nous
+ exposerons ces idées et les illustrerons sur une problématique issue
+ des neurosciences, où la question posée consiste à clarifier le rôle
+ des astrocytes pathologiques dans le développement de la dépression
+ corticale propagée.<br> Il s'agit de travaux en cours, menés en
+ collaboration entre une équipe de calcul formel (Univ. Lille/CRIStAL),
+ une équipe de modélisation en mathématiques appliquées (Univ. Le
+ Havre/LMAH) et une équipe de neurobiologie (Rouen/INSERM).<br><br>
+
+ <b>Clémence Frioux (Inria Rennes, Dyliss group)</b> - <i>Hybrid
+ gap-filling to reconcile qualitative and quantitative abstractions of
+ metabolism.</i><br> As the amount of genomics data keeps on increasing,
+ so is the need to develop efficient techniques to model the
+ metabolism. An important part of the modelling is the gap-filling step
+ during the reconstruction of metabolic networks. It aims to complete a
+ draft metabolic network based on experimental evidence by selecting
+ adequate reactions from a database to restore the observed metabolic
+ behaviour. Most of the existing techniques to achieve this procedure
+ are quantitative and use linear programming to model flux distribution
+ in the networks. They need accurate data and some linear problems are
+ not solvable without a relaxation of constraints due to the space of
+ solutions. We thus developed lately a topological method called meneco
+ that can perform gap-filling when few data is available. Based on
+ Answer Set Programming (ASP), it takes advantage of efficient solvers
+ to easily sample the space of solutions, but the outputs are often not
+ flux-balanced. Thanks to the progress in the development of ASP, it is
+ now possible to plug a linear programming solver to the ASP one. Here
+ we propose a hybrid method to take the most out of the two modellings
+ and use the efficiency of the topological solving to reach
+ quantitative-compliant solutions for gap-filling. This hybrid solving
+ enables to take into account new constraints and hypotheses about the
+ dynamic system (initial state, steady state, inputs/outputs) and can
+ be relevant to solve new metabolic networks modelling
+ problems.<br><br>
+
+ <b>Emilie Allart (University of Lille, CRIStAL lab)</b> - <i>Elementary
+ modes refine abstract interpretation of reaction networks with partial
+ kinetic information.</i><br> Genetic engineering is nowadays widely
+ used to change the genome of cells in order to modify their behaviour,
+ for example to overproduce some metabolite of interest. Gene knockout
+ is one common genetic engineering technique which consists in removing
+ one or more genes from the genome. Given the combinatorial number of
+ possible genetic changes and the consequent impossibility of testing
+ all of them by wet lab experiments, in silico prediction of the most
+ interesting genetic modifications is often desirable. However, the
+ necessary information for the in silico modelling of the organism of
+ interest is usually lacking. This is the case for example for the
+ bacterium B. Subtilis: its genetic engineering for the overproduction
+ of surfactin (a well-known antibiotic [1]) is of high interest in
+ agriculture among other fields, but the lack of information about the
+ biochemical functioning of this organism prevented us from modeling it
+ with the existing methods. In order to overcome this problem, we
+ developed a modeling language to represent reaction networks with
+ partial kinetic information, and a method based on abstract
+ interpretation that allows us to analyse models written in this
+ language even in the absence of quantitative information [2, 3]: from
+ the steady state semantics of the reaction network, we transform
+ arithmetic constraints into abstract constraints over a finite domain
+ where unknowns have been abstracted away. The qualitative behaviour of
+ the system can therefore be evaluated in silico by means of a
+ constraint solver, which gives us the set of all solutions
+ (corresponding to combinations of feeding changes and gene knockouts)
+ that lead to the desired behaviour. However, abstract interpretation
+ usually over-approximates the solution space. As an example, while a
+ finite system of linear equations implies all the equations that can
+ be expressed as a linear combination, this implication no longer holds
+ once the equations have been translated into abstract constraints. My
+ current work consists in optimally generating new arithmetic
+ constraints from the existing in order to minimize the solution
+ space. Here I will discuss in particular our interest in elementary
+ flux modes [4] and how to adapt classical elementary mode analysis to
+ the generation of abstract constraints to improve the qualitative
+ analysis of reaction networks with partial kinetic
+ information.<br><br>
+
+ <b>Marie Beurton-Aimar (University of Bordeaux, Labri lab)</b>
+ - <i>How to display patterns inside elementary flux modes.</i> <br>
+ Elementary flux modes computing is a powerful tool to identify
+ feasible routes trough a metabolic network(Schuster et
+ al. Nat. Biotechnol., 18 (3), 2000). According to their definitions,
+ the list of all elementary flux modes provides a way to analyze the
+ behaviors of the network both in its current functioning and under
+ perturbations. But the main problem with this tool remains the size of
+ this list. Currently a metabolic network with approximatively 50
+ reactions and metabolites can generate more than several thousands of
+ elementary flux modes. To be useful, new tools to automatically
+ analyze the results are required. In a first attempt, we have used the
+ computing of Minimal Cut Sets (Gagneur J., Klamt S. BMC
+ Bioinformatics, 2004) of all elementary flux modes to identify
+ patterns, i.e. list of common reactions, in the set of elementary flux
+ modes . To visualize these patterns which can be see as a tree of
+ sub-patterns we have chosen a technique coming from the domain of
+ large data set visualization, the parallel coordinates
+ displaying. This technique presents data as flux through variables put
+ on the x axis. Values of the flux is on the y axis. In our case,
+ reactions are put on the x axis and elementary flux modes are data
+ lines or edges between reactions. The reaction stoichiometry values
+ are set on the y axis: 1 if the reaction is present in forward
+ direction in this elementary flux mode, 2 if it is in backward
+ reaction and 0 if the reaction is absent. The flux size between two
+ reactions informs about the quantity of elementary flux modes which
+ use the same association between two reactions. The figure below shows
+ a result example of patterns sharing by the elementary flux modes in
+ the plant cell metabolism (Beurton-Aimar et al. Plant Metabolic Flux
+ Analysis, 2013). The displaying tool CoPHI is accessible on this
+ website http://www.labri.fr/perso/jsansen/. The display is dynamique,
+ users can change the order of the reactions on x axis and so explore
+ by themselves the patterns that can be found. Information given by MCS
+ computing is used to sort reactions by taking into account set of
+ reactions. Future works will be done to develop a CoPHI version
+ dedicated to metabolic network analysis.<br><br>
+
+ <b>Florian Bridoux (University of Marseille, LIF lab) </b> - <i>On The
+ Cost Of Simulating A Parallel Boolean Automata Networks By A
+ Sequential One.</i><br> In this presentation, we study Boolean
+ automata networks (BANs). A given BAN can be associated with several
+ dynamics, depending on the schedule (i.e. the order) we choose to
+ update its automata. In this presentation, we consider all
+ block-sequential update schedules: we group automata into blocks, and
+ we update all automata of a block at once, and iterate the blocks
+ sequentially. For the last 15 years, people have studied the influence
+ of the update schedules on the dynamics of a BAN. Here, we do the
+ opposite. We want to determine the minimum number κ of additional
+ automata that a BAN associated with a given block-sequential update
+ schedule needs to simulate a given BAN with a parallel update
+ schedule. To solve this problem, we introduce a graph that we call
+ confusion graph built from the BAN and the update schedule. We show
+ the relation between κ and the chromatic number of the confusion
+ graph. Thanks to this confusion graph, we bound κ in the worst case
+ between n/2 and 2n/3 + 2 (n being the size of the BAN simulated) and
+ we conjecture that this number equals n/2. We support this conjecture
+ with two results: the clique number of a confusion graph is always
+ less than or equal to n/2 and, for the subclass of bijective BANs, κ
+ is always less than or equal to n/2.<br><br>
+
+ <b>Aurélien Naldi (University of Montpellier, DIMNP lab)</b>
+ - <i>Reversed logical models for the study of basins of
+ attraction.</i><br> Boolean, and more generally logical, models are
+ widely used for the study of biological systems, especially
+ differentiation systems. The formal identification of their attractors
+ has been widely studied, however the corresponding basins of
+ attraction received less attention.<br> In this purpose, we propose a
+ method to construct a "reversed" model regarding the asynchronous
+ dynamical behaviour: the successors of the states of a reversed model
+ correspond to their predecessors in the original one. While the
+ reversal can be generalized only to a particular class of multivalued
+ models, we show how to use model booleanization to study the reversed
+ asynchronous dynamics of any model.<br> The study of the reversed
+ dynamics then facilitates the identification of the basins of
+ attraction, assuming that we already know the attractors. We can
+ further compute "strong" and "weak" basins (from which we can
+ respectively reach a unique or multiple attractors), as well as their
+ "frontiers", i.e., the sets of states such that the reachable
+ attractors are different from the attractors reachable from
+ neighbouring states (predecessors or successors).<br> This approached
+ is illustrated on a published model of the cell fate decision in
+ response to death-receptor engagement.<br><br>
+
+ <b>Pierre Siegel (University of Marseille, LIF lab)</b> - <i>Des logiques
+ non-monotones aux systèmes dynamiques discrets (SDD).</i><br> Du point
+ de vue logique et représentation des connaissances, un système
+ biologique peut être considéré comme un ensemble de variables qui
+ interagissent. Dans ce cadre la cellule pose des problèmes
+ intéressants à l’Intelligence Artificielle. Il faut d’abord formaliser
+ les interactions mais une formalisation en logique « classique » est
+ difficile et donne des incohérences. Ensuite ce que l’on sait vient en
+ bonne partie d’expériences. On ne connaît donc qu’une petite partie
+ des interactions et cette connaissance peut être révisable,
+ incertaine, contradictoire et même fausse. On veut aussi essayer de
+ compléter in-silico les interactions. Enfin la complexité algorithme
+ est importante. Ces problèmes sont étudiés depuis longtemps en IA en
+ utilisant des logiques non-monotones et des techniques de
+ programmation par contraintes. Dans le cas particulier de la cellule
+ des résultats ont été obtenus en utilisant la logique des défauts
+ [1].<br> D’un autre côté, les systèmes biologiques peuvent être
+ regardés dans le contexte des réseaux d’automates et des SDD. Des
+ théorèmes importants portent sur les cycles d’interactions dont
+ l’étude est fondamentale. Mais une représentation des SDD par la
+ logique des défauts (et par la plupart des logiques non-monotones)
+ n’est pas adaptée ; par exemple l’équivalent d’un circuit négatif n’a
+ pas d’extension (de solution, de point fixes…) Cette absence
+ d’extension pour les logiques des défauts a été étudiée [2,3] et cette
+ étude a donné la Logique des Hypothèses. Pour cette logique on a
+ toujours des extensions mais certaines d’entre elles, les extensions
+ fantômes, bien caractérisées sont spéciales (et leur utilité n’était
+ pas claire).<br> Nous étudions une représentation des SDD par la
+ Logique des Hypothèses. Un but est de permettre de discriminer les
+ états stables, les cycles stables et les cycles instables. Les
+ extensions fantômes semblent permettre de le faire. Un autre but est
+ de donner des algorithmes efficaces pour calculer les états et
+ cycles. Quelques premiers résultats seront présentés.<br><br>
+
+ <b>Laurent Trilling (University of Grenoble, TIMC-IMAG lab)</b>
+ - <i>Apport de la non monotonie pour la modélisation logique de
+ réseuax de régulation génique.</i></br> Nous nous intéresserons à la
+ modélisation déclarative des réseaux logiques de régulation génique
+ proposés par René Thomas, à l'aide de la technologie de programmation
+ logique ASP (Answer Set Programming). Cette technologie est basée sur
+ une logique non monotone n'autorisant que certains types de modèles
+ logiques, dits stables: intuitivement, ne sont vrais que les atomes
+ qui sont prouvables grâce aux axiomes. Un modèle stable est minimal en
+ ce sens que la suppression d'un atome du modèle ne peut résulter en un
+ modèle. La technologie ASP connait un certain engouement à l'heure
+ actuelle, assurément dû au pouvoir d'expression des langages proposés
+ et aussi aux performances des solveurs associés. Mais l'intérêt de
+ cette propriété de non monotonie et son impact sur la modélisation
+ d'applications en termes de méthodologie et d'optimisation ne sont pas
+ toujours soulignés précisément. Nous tâcherons de le faire dans le
+ cadre de la modélisation des réseaux de Thomas.<br> Par approche
+ déclarative, nous entendons : représentation de toutes les données
+ biologiques disponibles (sur la structure ou la dynamique), sous forme
+ d'axiomes logiques(contraintes) et obtention sous forme intensionelle
+ (implicite) des réseaux de Tomas cohérents avec ces données en cas de
+ satisfaisabilité. Dans le cas contraire, une procédure automatique de
+ réparation justifiée doit être applicable. L'approche fournit, entre
+ autres, une faculté d'apprentissage appréciable : celle de tous les
+ théorèmes, restreints à une formulation fixée (par exemple les clauses
+ d'une taille limitée sur un ensemble déterminé d'atomes), déductibles
+ de ces modèles.<br> Deux avantages généraux d'ASP sont reconnus : un
+ traitement des données incomplètes radical (les atomes non prouvables
+ sont réputés faux) et un pouvoir de déduction augmenté (les modèles
+ stables forment un sous-ensemble des modèles). Nous aborderons plutôt
+ trois aspects précis, concernant la modélisation étudiée : 1)
+ l'utilisation de défauts pour spécifier la réparation d'inconsistance
+ en soulignant l'intérêt d'exprimer la minimisation sous-jacente en
+ termes logiques (et non pas algorithmiques), 2) l'emploi (et la
+ méthodologie de construction) d'une conjonction de défauts originale
+ pour exprimer la notion de compositions d'interactions géniques
+ seulement généralement vraies (sauf à être prouvée effectivement
+ fausses), 3) la mise en œuvre de formules CTL générales, en
+ particulier du type AF (indispensables pour exprimer des propriétés
+ exhaustives sur les comportements), en prenant appui sur la minimalité
+ des modèles stables.<br><br>
+
+ <b>Anaïs Baudot (Institut de Mathématiques de Marseille)</b>
+ - <i>Mining and modeling biological networks to study rare and common
+ human diseases.</i><br> Networks are scaling-up the analysis of gene
+ and protein functions, hence offering new avenues to study the
+ diseases in which these genes and proteins are involved. In this
+ context, I will talk about the exploration of interactome networks
+ containing thousands of physical and functional interactions between
+ proteins. We develop partitioning algorithms to recover communities –
+ or functional modules – from these large-scale networks, and use them
+ to study the cellular functions of proteins of interest. Recently, we
+ have extended the community detection to multiplex networks, i.e.,
+ networks containing different layers of different interaction
+ categories, such as protein-protein interaction or gene
+ co-expression. I will show how these approaches can be used to study
+ the relationships between different common diseases, in particular
+ their inverse comorbidities. The last part of my talk will be
+ dedicated to our recent work on random walks with restart on
+ multiplexes, in order to study rare monogenic diseases. I will
+ finally briefly mention logical modeling to decipher drug synergies in
+ cancer, and how mining large-scale static networks and modeling
+ smaller-scale dynamical network could unite around expression data
+ integration.<br><br>
+
+ <b>Jean Coquet (Inria Rennes, Dyliss group)</b> - <i>Analysis of TGF-β
+ signaling networks to find different families of trajectories.</i><br>
+ The Transforming Growth Factor TGF-β is a multifunctional cytokine
+ that regulates mammalian development, differentiation, and homeostasis
+ (Ikushima and Miyazono, 2011). As a growth inhibitor of epithelial,
+ endothelial, and hematopoietic cells, TGF-β is a potent anticancer
+ agent in normal tissue. At the opposite TGF-β acts as a promoter of
+ tumor by inducing the hallmarks of the cancer. The context-dependent
+ pleiotropic nature of TGF-β is associated with complex signaling
+ pathways. Our group recently developed a model for TGF-β- dependent
+ signalling based on the integration of the 137 signaling pathways from
+ Pathway Interaction Database (Andrieux et al, 2014). Based on guarded
+ transition formalism, we identified 15,934 chains of reactions
+ (trajectories) linking TGF-β to at least one of 159 target genes. The
+ combined size and complexity of this model place it beyond current
+ understanding. Its analysis require automated tools for identifying
+ general patterns. In this study, we focused on designing a reasoning
+ method to characterize the composition of these trajectories. We used
+ an exhaustive and without prior assumptions approach to explore
+ them. First, we grouped the trajectories into several families using
+ the Relevant Set Correlation model (Houle, 2008). Then, we extracted
+ the over-represented molecules for each families. Finally, we
+ identified pairs of over-represented molecules included in the same
+ trajectories but not described in pathway databases. The main results
+ of the study were the identification of two trajectory families
+ representing different TGF-β-dependent signaling pathways, and the
+ prediction of hundreds of molecule pairs occurring in trajectories and
+ never reported in signaling databases.<br><br>
+
+ <b>Arnaud Poret (Ecole Centrale de Nantes, LS2N lab)</b> - <i>Linking
+ Cancer Models with Therapeutic Effects.</i><br> The goal is to model
+ the signaling pathways involved in the drug response of cancerous
+ cells to find how to counteract resistance. We have the gene
+ expression profile (GEP) of several breast cancer and multiple myeloma
+ cells treated with various drugs. Along with the GEPs, we also have
+ the phenotypic drug response of the cells in term of survival
+ percentage. Building models of the signaling pathways involved in
+ drug response/ resistance could allow us to predict molecular
+ interventions able to counteract resistance, and then decrease the
+ survival of resistant cells. For reconstructing these signaling
+ pathways from the GEPs, we will use existing tools such as
+ PID2SIF2Graph and MCWalk, operating on public databases such as KEGG,
+ Reactome, Wikipathways and NCI-PID. For modeling the reconstructed
+ networks, we envision two approaches: logical programming, namely
+ answer set programming, and Boolean networks. The obtained models
+ will be used to assess several molecular interventions in their
+ ability at decreasing the predicted survival percentages.<br><br>
+
+ <b>Amos Korman (University Paris Diderot, IRIF lab)</b>
+ - <i>Confidence sharing: an economic strategy for efficient
+ information flows in animal groups.</i><br> Social animals may share
+ information to obtain a more complete and accurate picture of their
+ surroundings. However, physical constraints on communication limit the
+ flow of information between interacting individuals in a way that can
+ cause an accumulation of errors and deteriorated collective
+ behaviors. In this talk, I will theoretically discuss a general model
+ of information sharing within animal groups, and take an algorithmic
+ perspective to identify efficient communication schemes that are,
+ nevertheless, economic in terms of communication, memory and
+ individual internal computation. I will present a simple algorithm in
+ which each agent compresses all information it has gathered into a
+ single parameter that represents its confidence in its
+ behavior. Confidence is communicated between agents by means of active
+ signaling. It turns out that this algorithm competes extremely well
+ with the best possible algorithm that operates without any
+ computational constraints.<br> The proofs rely on the Cramer-Rao bound
+ and on our definition of a Fisher Channel Capacity. These concepts are
+ used to quantify information flows within the group and to obtain
+ lower bounds on collective performance. The results suggest confidence
+ sharing as a central notion in the context of animal
+ communication.<br> The talk is based on a paper published in PLoS
+ Computational Biology.<br><br>
+
+ <b>Oder Feinerman (Weizmann institute of science)</b> - <i>Algorithmic
+ challenges in ant cooperative transport.</i><br> Ants that encounter a
+ large food item may join forces to cooperatively carry it towards the
+ nest. To do this, the ants must coordinate their forces while
+ simultaneously navigating towards the nest, often through complex
+ environments.This talk will discuss the behavioral algorithm that
+ allows that ants to achieve this non-trivial feat. I will show how the
+ ants' carrying algorithm allows the group to efficiently extract
+ useful information that is made available by the navigationally
+ competent individuals. Furthermore, I will show how the same simple
+ algorithmic laws lead to the emergence of alternative group-level
+ solutions when individual ant capabilities do not suffice.<br><br>
+
+ <b>Jonathan Behaegel (University of Nice, I3S lab)</b> - <i>Réseaux
+ génétiques hybrides: de la logique de Hoare à l'identification de
+ paramètres.</i> <br>La modélisation de la dynamique des réseaux de
+ gènes repose sur des paramètres qui reproduisent les comportements du
+ système. Lorsque le temps considéré évolue de manière continu (temps
+ chronométrique), l'identi cation des paramètres devient extrêmement di
+ cile. Il devient alors nécessaire de prendre en compte de nouvelles
+ contraintes provenant des données biologiques qui représentent, dans
+ la plupart des cas, des durées entre des événements observés. Pour
+ prendre en compte de telles durées, nous étendons le cadre de mod-
+ élisation de René Thomas en considérant que chaque état qualitatif
+ devient un sous-domaine de l'espace des concentrations dans lequel les
+ trajectoires évoluent de manière continue. Ainsi, chaque trajectoire
+ met un temps non nul à traverser chaque sous-domaine. Il s'agit d'une
+ classe particulière d'automates hybrides linéaires dont les paramètres
+ sont appelés "célérités". <br> Les traces biologiques observées
+ (succession d'états qualitatifs combinés avec des durées) peuvent
+ alors être interprétées comme une exécution de programme impératif, et
+ la logique de Hoare munie du calcul de la plus faible précondition
+ nous permet de construire des contraintes sur les paramètres du
+ modèle. Nous illustrons ce calcul de la plus faible précondition sur
+ un modèle simpli é du cycle circadien. <br><br>
+
+ <b>Celia Biane (University of Evry, Ibisc lab)</b> - <i>Inférence
+ d'action sur les réseaux pour la reprogrammation cellulaire.</i><br>
+ La reprogrammation consiste à modifier un système dynamique initial
+ afin d’atteindre ou d’éviter certains comportements. En biologie, la
+ reprogrammation cellulaire est étudiée notamment dans le cas du Cancer
+ et de l’étude des cellules souches. Par exemple, au cours de la
+ tumorigenèse, des évènements génétiques et épi-génétiques sont
+ responsables d’une transition d’une cellule saine à une cellule
+ cancéreuse et les médicaments reprogramment la cellule cancéreuse vers
+ la mort cellulaire. Un défi majeur est l’identification, sur
+ l'interactome (réseau biologique décrivant les interactions entre
+ molécules), des perturbations causales de la maladie, et inversement,
+ des cibles d’action des médicaments. Dans ce cas, la reprogrammation
+ se caractérise par des actions structurelles de délétions ou de
+ destruction de sous-ensembles de nœuds ou d’arcs responsables de
+ transitions de comportement cellulaire. L'inférence d’actions causales
+ est combinatoire et constitue un problème inverse à une simulation de
+ perturbations structurelles responsables de modifications de la
+ dynamique du réseau moléculaire. Dans cette présentation, nous
+ décrirons la formalisation de ce problème par le gain et la perte de
+ propriétés à l’équilibre dans un réseau booléen, nous présenterons des
+ méthodes algorithmiques d’inférence d’actions sur les réseaux et
+ montrerons un exemple d’application de ces méthodes pour la prédiction
+ de «drivers» (évènements génétiques initiant un processus cancéreux)
+ et de cibles thérapeutiques dans le cas du Cancer du Sein.
+ Reprogrammation cellulaire, Problème Inverse, Inférence d’actions
+ structurelles, Réseaux Booléens, Dynamique moléculaire,
+ Cancer.<br><br>
+ </p>
+
+ <span style="font-weight: bold;"><br>
+ </span><h3>Participants</h3>
+
+ <p style="text-indent:2em" align="justify"></p>
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+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
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+ <h1>Journées annuelles du groupe de travail (4ème édition)</h1>
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ Date : Lundi 2 (toute la journée) et mardi 3 juillet (matin) 2018
+ </p>
+ <p align="justify">
+ Lieu : site Saint-Charles de l'université d'Aix-Marseille à Marseille.
+ </p>
+
+ <p align="justify">
+ Organisateurs : Elisabeth Remy, Grégory Batt, Cédric Lhoussaine et Anne Siegel
+ </p>
+
+
+<p align="justify">
+ La quatrième édition des journées annuelles du GT Bioss va
+ se dérouler juste avant les Journées Ouvertes Biologie, Informatique et Mathématiques (<a href="https://jobim2018.sciencesconf.org/">JOBIM</a>).
+<div class=""><br class="">
+</div>
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+ L'inscription, gratuite mais obligatoire, se fait en
+ remplissant le formulaire
+ accessible <a href="https://docs.google.com/forms/d/1adQlZhN32sjrXwBPoM8ISrscn0CegW89TOKhnvVf9AY/viewform?edit_requested=true">ici.</a>
+ </p>
+
+ <h3>Orateurs invités</h3>
+
+<p align="justify">
+ <meta charset="utf-8"><span style="font-size:14.666666666666666px;font-family:Arial;color:#000000;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;"
+ id="docs-internal-guid-34c540d9-cafa-f741-0321-10cf7fc90e08"></span>
+<a href="http://www-sop.inria.fr/members/Olivier.Bernard/">Olivier Bernard</a>, équipe projet BioCore, Inria.<br>
+<a href="http://lifeware.inria.fr/wiki/Fages/HomePage">François Fages</a>, équipe projet Lifeware, Inria.<br>
+<a href="https://www.cimr.cam.ac.uk/research/affiliated/gottgens">Bertie Gottgens</a>, Cambridge Institute for Medical Research. <br>
+<a href="http://page.mi.fu-berlin.de/hsiebert/">Heike Siebert</a>, DFG-Research Center Matheon, Berlin.<br>
+ </p>
+
+
+ <h3>Programme</h3>
+ <h4>Lundi 2 Juillet</h4>
+ <p align="left">
+ <b>10h00-10h45</b> - <b>Conférencière invitée</b> - Heike Siebert (Université de Berlin)- <i>A Boolean Look at Synthetic Biology - Finding Cell Classifiers Using Answer Set Programming</i><br>
+ <b>10h45-11h05</b> - Adrien Richard (I3S, Nice) - <i>Fixing monotone boolean networks asynchronously</i><br>
+ <b>Pause</b><br>
+ <b>11h30-11h50</b> - Stephanie Chevalier (LRI) - <i>A logical approach to identify Boolean Networks that model cell differentiation</i><br>
+ <b>11h55-12h15</b> - Maxime Folschette (Irset/Irisa) - <i> GULA: Semantics-Free Learning of a Biological Regulatory Networks from a Synchronous, Asynchronous or Generalized State Graph</i><br>
+ <b>12h20-12h40</b> - Aurelien Naldi (ENS Paris) - <i> Similarities and complementarity of positive feedback circuits and stable motifs in logical regulatory networks </i><br><br>
+
+ <b>12h40- 14h00 - Pause déjeuner</b><br><br>
+
+ <b>14h00-14h45</b> - <b>Conférencier invité</b> -Bertie Göttgens (Cambridge institute for Medical Research) - <i> Reconstructing Cell States, Lineage trajectories and Regulatory Networks from Single cells Molecular profiles.</i><br>
+ <b>14h50-15h10</b> - Alberto Valdeolivas (I2M) - <i> A Multiplex Network approach to Premature Aging Diseases</i><br>
+ <b>Pause</b><br>
+ <b>15h30-15h50</b> - Céline Hernandez (ENS Paris) - <i> Dynamical modelling of T cell co-inhibitory pathways to predict anti-tumour responses to checkpoint inhibitors</i><br>
+ <b>15h50-16h10</b> - Eugenia Oshurko (LIP) - <i> Representation and aggregation of cellular signalling knowledge in KAMI</i><br>
+ <b>16h15-16h35</b> - Sébastien Légaré (LIP) - <i>Biocuration and rule-based modelling of protein interaction networks in KAMI</i><br>
+ <b>Pause</b><br>
+ <b>17h00-17h45</b> - <b>Conférencier invité</b> - François Fages (Inria Saclay) - <i>Computer-aided biochemical programming of synthetic micro reactors as diagnostic devices</i><br>
+ <b>17h50-18h10</b> - Loic Pauleve (LRI) - <i>The CoLoMoTo Interactive Notebook: Accessible and Reproducible
+ Computational Analyses for Qualitative Biological Networks</i></br>
+ <b>18h15-18h35</b> - Discussion Bioss.<br>
+ </p>
+
+ <h4>Mardi 3 Juillet</h4>
+ <p align="left">
+ <b>09h00-09h45</b> - <b>Conférencier invité</b> - Olivier Bernard (Inria Nice Sophia Antipolis) - <i>Dynamical Reduction of Metabolic Networks. Application to Microalgae</i><br>
+ <b>09h50-10h10</b> - Nils Giordano (LS2N) - <i>Using co-activity networks to reveal the structure of planktonic symbioses in the global ocean</i><br>
+ <b>Pause</b><br>
+ <b>10h30-10h50</b> - Ghuvan Grimaud (Biomathematica) - <i>EvoDRUM: an evolutionary systems biology framework to investigate the origin of early metabolisms</i><br>
+ <b>10h55-11h15</b> - Anne Siegel (IRISA) - <i> Learning boolean rules for the regulatory control of metabolism: a case study </i><br>
+ <b>11h20-11h40</b> Thibault Etienne (Ibis, Inria) - <i>Coordination of mRNA stability and cell physiology in bacteria: a modelling study</i>
+ </p>
+ <h3>Résumés</h3>
+ <p align="justify">
+ <b>Stephanie Chevalier (LRI)</b> - <i>A logical approach to identify Boolean Networks that model cell differentiation.</i><br>
+ <a href="./Resume_ChevalierStephanie.pdf">Résumé</a><br><br>
+
+ <b> Thibault Etienne (Ibis, Inria)</b> - <i>Coordination of mRNA stability and cell physiology in bacteria: a modelling study</i><br>
+ Thibault Etienne, Laurence Girbal, Muriel Cocaign-Bousquet, Delphine Ropers<br>
+ The adaptation of bacterial physiology to environmental fluctuations involves system-wide changes of metabolism and gene expression. This reprogramming of the cell takes place at two different levels: on a global scale through the adjustment of the level and activity of components of the gene expression machinery (ribosomes...), and locally, through the adjustment of the concentration of regulators specifically coordinating the cell response to the new environmental conditions.<br>
+ The different regulatory levels are interlaced and form large biochemical networks, whose dynamic functioning is not intuitive. Among these regulatory levels, recent studies have shown that post-transcriptional regulations are more important than usually assumed. Contrary to the often-made assumption in bacteria, protein and mRNA levels are not proportional and mRNA stability (typically a few minutes) varies with the translational activity, the cell growth rate and the concentration of regulators (small RNAs, HFQ,...). How these interlocked control mechanisms adjust mRNA half-life to cell physiology remains largely unknown. In our study, we tackle this question by means of mathematical modelling using available times-series -omics data in Escherichia coli (transcriptomics and stabilomics). Our objective is to provide a mechanistic explanation of mRNA degradation profiles obtained at various growth rates.<br>
+ We develop a structural model of mRNA degradation in E.coli based on Michaelis-Menten kinetics. In this model, the individual parameters vary with the nature of the mRNA and the cell growth rate. A mixed-effect modelling framework is used to take into account the variability of these parameters: using the genome-wide - omics data, we estimate the mean parameters describing the population of mRNAs and the variance parameters, which allow to reproduce the degradation profile of each mRNA in each condition. The analysis of mean parameter values informs us on the global regulatory effects, while the parameter variances reflect the specific regulatory mechanisms.<br><br>
+
+ <b>Maxime Folschette (Irset/Irisa)</b> - <i> GULA: Semantics-Free Learning of a Biological
+ Regulatory Networks from a Synchronous, Asynchronous or Generalized State Graph</i><br>
+ The automatic learning of an interaction graph from the sole observation
+ of its dynamics is an ongoing challenge. An example is the existing LFIT
+ algorithm which learns and refines logic rules representing a model, from
+ a set of state transitions representing its dynamics. Starting from the
+ learning of purely deterministic synchronous Boolean systems, several
+ versions have been developed in order to tackle dynamics with memory,
+ inconsistencies or with multi-valued variables. However, all of them rely
+ on the knowledge of the underlying semantics, that is, the update scheme
+ of the variables. This work intents to free the learning process from this
+ knowledge.<br>
+ With GULA (General Usage LFIT Algorithm), we focus on three different semantics: synchronous (all
+ variables must update their value between two discrete time steps),
+ asynchronous (exactly one variable must do so) or general (any subset of variables may do so). The learning presented here is based on the refinement of logic
+ rules (a set of conditional atoms and a conclusion atom) that
+ represent the possibility for a variable to change its value under some
+ conditions on the current state. Such rules only represent the
+ potentiality of a change, which makes them independent of the semantics.
+ Nevertheless, we also exhibit some properties that characterize the
+ dynamics of the three given semantics, allowing to correctly interpret the
+ rules of the final regulatory graph.<br>
+ This work opens many outcomes. The most pressing is finding a broader
+ characterization of what a “learnable” semantics is, allowing to
+ generalize the scope of this approach. Furthermore, the semantics itself
+ could be learned along with the rules, allowing to entirely learn a
+ system. Finally, getting rid of the arbitrary but mandatory discretization
+ step would allow to directly learn from the gene expression measurements,
+ as already proposed with ACEDIA.<br><br>
+
+ <b>Ghuvan Grimaud (Biomathematica)</b> - <i>EvoDRUM: an evolutionary systems biology framework to investigate the origin of early metabolisms</i><br>
+ Ghjuvan Grimaud, Elena Litchman, Christopher Klausmeier<br>
+ The origin of the fundamental metabolic pathways and the subsequent rise
+ of the great metabolic diversity of microbes are two major steps in life’s
+ evolution on Earth and potentially other habitable planets. Understanding
+ how different metabolisms may arise, what conditions select for different
+ types of metabolic networks, and how they assemble to form ecological
+ communities are key questions for the origin of life. The evolutionary
+ emergence of diverse metabolisms depends not only on environmental
+ conditions but also on microbial interactions such as competition and
+ mutualism. Ecological interactions are being increasingly recognized as a
+ driving force of evolutionary diversification in different groups of
+ organisms, including microbes [1]. So far, the role of microbial interactions
+ in the origin of metabolic pathways under dynamic conditions has not been
+ investigated in detail. Here we propose to combine two novel modeling
+ approaches from two disparate disciplines (systems biology and evolutionary ecology) to
+ explore how microbial metabolic networks arise and evolve in dynamic
+ community contexts. We embed a recently developed metabolic modeling
+ approach for the elementary flux mode analysis under nonequilibrium
+ conditions (the Dynamic Reduction of Unbalanced Metabolism, DRUM[2]) in an
+ eco-evolutionary modeling framework of trait evolution (Adaptive Dynamics [3,4]) to
+ investigate how different metabolic networks arise and compete in
+ different environments. The resulting new Evolutionary Systems Biology
+ mathematical framework (evoDRUM) is
+ a powerful tool that allows extensive explorations of how early
+ metabolisms appeared and were maintained by natural selection and, thus,
+ is useful for the field of early microbial evolution. EvoDRUM extends and
+ modifies the idea of gathering the evolutionarily possible reactions by
+ defining a large — ideally universal — mutation space in which evolution
+ can proceed[5]. In line with the Adaptive Dynamics framework, evolution
+ occurs by a step-by-step mutant/resident invasion dynamics, with a defined
+ mutation rate. The novelty of the proposed approach is that it
+ investigates the metabolically explicit trait changes and evolution as a
+ result of selection through competitive interactions of different
+ phenotypes, and allows the incorporation of metabolite accumulation and
+ evolutionary innovations. First applied to simple metabolic networks with
+ several resources and temporally fluctuating conditions, we then use it
+ for genome-scale metabolic networks.<br>
+ <i>References</i><br>
+ 1. Brodie, J., Ball, S.G., Bouget, F.-Y., Chan, C.X., De Clerck, O., Cock, J.M., Gachon, C., Grossman, A.R., Mock, T., Raven, J.A., Saha, M., Smith, A.G., Vardi, A., Yoon, H.S., and Bhattacharya, D. (2017). Biotic interactions as drivers of algal origin and evolution. New Phytologist 216, 670-681.<br>
+ 2. Baroukh C., Munoz-Tamayo R., Steyer J.P. and Bernard O. (2014). DRUM: A new framework for metabolic modeling under non- balanced growth. Application to the carbon metabolism of unicellular microalgae. PloS one, 9 (8), e104499.<br>
+ 3. Dieckmann U. and Law R. (1996). The dynamical theory of coevolution: A derivation from stochastic ecological processes. Journal of Mathematical Biology, 34, 579-612.<br>
+ 5. Geritz S., Kisdi E., Meszéna G. and Metz J. (1998). Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evolutionary Ecology, 12, 35-57.<br>
+ 5. Szappanos B., Fritzemeier J., Csörgo B., Lazar V., Lu X., Fekete G., Balint B., Herczeg R., Nagy I., Notebaart R.A. et al. (2016). Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nature communications, 7.<br><br>
+
+ <b>Céline Hernandez (ENS Paris)</b> - <i>Dynamical modelling of T cell co-inhibitory pathways to predict anti-tumour responses to checkpoint inhibitors</i><br>
+ Céline Hernandez(1), Aurélien Naldi(1), Wassim Abou-Jaoudé(1), Guillaume Voisinne(2), Romain Roncagalli(2), Bernard Malissen(2), Morgane Thomas-Chollier(1), Denis Thieffry(1)<br>
+ (1) <i>Computational Systems Biology team, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Université, 75005 Paris, France</i><br>
+ (2) <i>Centre d'Immunologie de Marseille-Luminy, Aix Marseille Université, INSERM U1104, CNRS UMR7280, 13288 Marseille, France</i><br>
+ In recent years, it has been recognised that T cells often display a reduced ability to eliminate cancer cells and that expression of co-inhibitors at their surface accounts for their compromised function. Antibodies blocking the functions of these co-inhibitors (checkpoint inhibitors) have become standard treatment for metastatic melanoma [1], leading to a revival in the study of T cell co-inhibitors. However, our understanding of the immunobiology of T cell co-inhibitors and of their harmful role during anti-tumour responses remains fragmentary. Despite some biochemical studies, a mechanistic understanding at the system-level of the modulation of T cell function by co-inhibitors has remained elusive.<br>
+ To overcome these limitations, we aim at delineating the mechanisms through which co-inhibitory molecules, such as PD-1 and CTLA-4, impede T cell functions at the system-level. To reach this goal, we use computational methods to map and model TCR co-signalling pathways, and ultimately predict cell responses to perturbations.<br>
+ First, we focused on the development of comprehensive annotated molecular maps (using the software CellDesigner [2]) based on the curation of scientific literature, in parallel with automated queries to public databases and protein-protein graph reconstruction. Next, using the software GINsim [3], these maps and protein networks are translated into a regulatory graph integrating current knowledge. The challenge is then to properly model concurrent intracellular processes, along with feedback control mechanisms. To cope with this complexity, we explored some network modules using a Rule-based formalism [4], in order to evaluate concurrent biological hypotheses and help specify logical rules recapitulating observed component behaviour back into the logical model. This model will be used to predict cell response to single or multiple perturbations, and thereby pave the way to the delineation of novel experiments, which will in turn be used to refine the maps and model.<br>
+ This integrated system-level view of the mechanisms of action of key T cell co-inhibitors in cancer will further provide a rationale for designing and evaluating drugs targeting T cell co-inhibitory pathways in anti-cancer immunotherapy.<br>
+ <i>References</i><br>
+ 1. Simpson TR, Li F, Montalvo-Ortiz W, Sepulveda MA, Bergerhoff K, Arce F, Roddie C, Henry JY, Yagita H, Wolchok JD, Peggs KS, Ravetch JV, Allison JP, Quezada SA (2013). Fc-dependent depletion of tumor-infiltrating regulatory T cells co-defines the efficacy of anti-CTLA-4 therapy against melanoma. The Journal of experimental medicine 210(9): 1695–710.<br>
+ 2. http://www.celldesigner.org/<br>
+ 3. http://www.ginsim.org<br>
+ 4. Feret J, Danos V, Krivine J, Harmer R, Fontana W (2009). Internal coarse-graining of molecular systems. Proceedings of the National Academy of Sciences of the USA 106(16): 6453-8<br><br>
+
+ <b>Sébastien Légaré (LIP)</b> - <i>Biocuration and rule-based modelling of protein interaction networks in KAMI.</i><br>
+ KAMI, the Knowledge Aggregator and Model Instantiator, is a software for
+ biocuration and modelling of molecular interaction networks. It provides a
+ knowledge representation to unambiguously express the details of
+ biomolecular interactions. This representation can be built either
+ programmatically or graphically via the KamiStudio interface. To assist
+ users in curating their biological knowledge, KAMI is organised in two
+ distinct layers: a network and a set of individual interactions called
+ nuggets. Once a new nugget is built, it can be automatically aggregated to
+ the network. The software then performs a series of tests to ensure
+ consistency including duplicate search, biological database grounding and
+ semantic checking. This greatly facilitates biocuration as users do not
+ need to have the complete network in mind to add new data. Furthermore,
+ interaction networks represented in KAMI can be directly converted to
+ rule-based models in the Kappa language for simulation and analysis. In
+ this talk, we will present the use of KAMI through a model of tyrosine
+ phosphorylation involved in cell signaling. This example is well suited to
+ showcase the advantages of the rule-based strategy. In particular, we will
+ demonstrate the use of causality analysis to discover pathways in the
+ model that were not explicitly input by the user.<br><br>
+
+ <b>Aurélien Naldi, ENS Paris</b> - <i>Similarities and complementarity of positive feedback circuits and stable motifs in logical regulatory networks</i><br>
+ Aurélien Naldi, Denis Thieffry<br>
+ Discrete qualitative models have been widely used to study complex biological regulatory networks. The increasing complexity of the systems of interest calls for efficient analysis methods, and in particular approaches directly relating the structure of the network to its dynamical properties.<br>
+ The study of feedback circuits, based on the seminal work of R. Thomas, is a prominent example of such approaches: positive circuits are associated to the co-existence of multiple attractors, while negative circuits are associated to sustained oscillations [1]. The properties of isolated circuits and of some simple combinations of circuits have been formally characterised [2], however their precise roles once embedded in complex networks remain unknown. An embedded circuit is called “functional” when the values of its regulators allow it to behave as an isolated circuit.<br>
+ Stable motifs (also called symbolic steady states) have recently been proposed to efficiently identify attractors of such models [3,4]. Each stable motif represent a partial assignment of model components such that all successors of the matching states also belong to the motif.<br>
+ The identification of stable motifs was recently added to the GINsim software [5], which already supported the identification of functional circuits. Based on the availability of these two analysis methods in the same software tool, we further explore the connection between the classical feedback circuits and stable motifs. The core of each stable motif is formed by a (group of) positive circuits settled in one of their two stable configurations. The resulting stability is often associated to functional positive circuits which can sustain their own functionality contexts. Stable motifs can further arise from non-functional positive circuits, which can be locked in only one of their two stable configurations.<br>
+ <i>References</i><br>
+ [1] Comet et al. (2013). On circuit functionality in boolean networks. Bulletin of Mathematical Biology 75: 906-19.<br>
+ [2] Remy et al. (2016). Boolean Dynamics of Compound Regulatory circuits. In : Rogato A, Zazzu V, Guarracino MR
+ (Eds.). Dynamics of Mathematical Models in Biology. Springer International Publishing, pp. 43-53.<br>
+ [3] Zañudo & Albert (2013). An effective network reduction approach to find the dynamical repertoire of discrete
+ dynamic networks. Chaos 23: 025111.<br>
+ [4] Klarner et al. (2014). Computing Symbolic Steady States of Boolean Networks. Lecture Notes in Computer
+ Sciences 8751: 561-70. [5] http://ginsim.org<br><br>
+
+ <b>Eugenia Oshurko (LIP)</b> - <i>Representation and aggregation of cellular signalling knowledge in KAMI</i><br>
+ Rule-based modelling has proven to be a successful approach for study- ing complex systems of cellular signalling. A rule-based language Kappa has been actively developed and used in recent years. However, building and curating big explanatory models using Kappa rules is challenging and cumbersome. To tackle exactly this problem we propose a bio-curation tool called KAMI (Knowledge Aggregator and Model Instantiator), which allows gradual semi-automatic aggregation of PPIs of different provenance, their annotation, visualisation and further instantiation to concrete rule-based models (including automatic generation of Kappa rules).<br>
+ Models in KAMI are accommodated using a specially designed graph- based knowledge representation system which provides robust mechanisms for incremental aggregation of partial knowledge, its audit, update, and transfer to various representations. In this talk we will present this knowl- edge representation system, its properties and the mechanisms for knowledge update based on graph rewriting. We will also focus on its instance used in KAMI to represent models of cellular signalling systems. Then we will speak about the strategy of automatic knowledge aggregation that exploits the properties of this system. And finally, we will show how KAMI uses domain-specific background knowledge (e.g. semantics of conserved pro- tein domains, definitions of protein families, splice variants and mutants) to sharpen aggregated models.<br><br>
+
+ <b>Loic Pauleve (LRI)</b> - <i>The CoLoMoTo Interactive Notebook: Accessible and Reproducible
+ Computational Analyses for Qualitative Biological Networks</i><br>
+
+ Joint work with A Naldi, C Hernandez, N Levy, G Stoll, P Monteiro, C
+ Chaouiya, T Helikar, A Zinovyev, L Calzone, S Cohen-Boulakia, D Thieffry<br>
+
+ The CoLoMoTo Interactive Notebook relies on Docker and Jupyter
+ technologies to provide a unified environment to edit, execute, share,
+ and reproduce analyses of qualitative models of biological networks.
+ To date, the framework provides access to software tools including Cell
+ Collective, GINsim, BioLQM, Pint, and MaBoSS. A Python interface has
+ been developed for each of these tools to offer a seamless integration
+ in the Jupyter web interface and ease the chaining of complementary
+ analyses.<br>
+ Website: http://colomoto.org/notebook<br>
+ Paper: http://doi.org/10.3389/fphys.2018.00680<br><br>
+
+ <b>Alberto Valdeolivas (I2M) </b> - <i> A Multiplex Network approach to Premature Aging Diseases.</i><br>
+ Premature aging (PA) syndromes are a group of heterogeneous rare disorders
+ that recapitulate some of the aspects associated to physiological aging.
+ They are caused by mutations in several genes involved in different
+ biological processes. Genes and proteins do not act isolated in cells but
+ rather interact in complex networks of molecular interactions. In this
+ context, we undertook a network approach to better understand the etiology
+ and pathophysiolgy of these diseases.<br>
+ First, we extracted the network modules surrounding genes mutated in PA
+ diseases, to define the landscape of biological processes that might be
+ perturbed. To this goal, we applied a strategy based on our recently
+ developed random walk (RW) with restart on multiplex networks [1]. This
+ allows us to navigate and extract information from different layers of
+ physical and functional interactions (e.g.,
+ protein-protein, co-expression, molecular complexes) outperforming
+ single-network approaches [1]. We captured modules representing the
+ hallmarks of physiological aging, and compared the processes commonly
+ perturbed in PA diseases, as well as those specific to a subset of
+ diseases.<br>
+ In a second part, we are developing a strategy to analyse the impact on
+ networks of PA disease-causing mutations. To this goal, we are performing
+ targeted attacks, removing from the multiplex network either genes (to simulate loss-of-function) or some of their interactions (to simulate "edgetic" mutations).
+ A modified version of our RW algorithm allows us to study the topological
+ modifications of the network after the attack, pinpointing to the most
+ affected genes, modules and processes.<br>
+ 1. Valdeolivas,A. et al. Random Walk With Restart On Multiplex And
+ Heterogeneous Biological Networks. 2017. bioRxiv.<br><br>
+
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+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
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+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
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+ <h1>Journée annuelle du GT Bioss (5ème édition)</h1>
+
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ <b>Date</b> : le 7 novembre 2019 de 9h30 à 17h30.
+ </p>
+ <p align="justify">
+ <b>Lieu</b> : Université Paris Diderot, salle 027C, au rez-de-chaussée de la Halle aux
+Farines. </br>Plans d'accès <a href="https://campus.univ-paris-diderot.fr/file/7495/download?token=ufRWvV6A">ici</a> et <a href="https://campus.univ-paris-diderot.fr/file/7492/download?token=Og2VmjpP">là</a>.
+ </p>
+
+ <p align="justify">
+ <b>Organisateurs</b> : Grégory Batt, Cédric Lhoussaine, Elisabeth Remy et Anne Siegel
+ </p>
+
+
+<p align="justify">
+ Cette cinquième édition des journées annuelles du GT Bioss va
+ se dérouler juste après la journée nationale du <a href="http://www.gdr-bim.cnrs.fr/">GDR BiM</a>
+ qui aura lieu le 6 novembre à l'Université Denis Diderot.
+</p>
+
+
+
+ <h3>Inscription</h3>
+ <p align="justify">
+ L'inscription, gratuite mais obligatoire, se fait via la <a href="http://www.gdr-bim.cnrs.fr/?page_id=160">page du GDR BiM de l'évènement</a>.</br>
+ La SFBI offre des bourses de voyage aux doctorants et postdocs (<b>deadline le candidature le 10 octobre!</b>)!
+ </p>
+
+ <h3>Orateurs invités</h3>
+
+ <p align="justify">
+ <a href="http://nuel.perso.math.cnrs.fr/">Gregory Nuel</a>, LPSM (Sorbonne Université). <br/>
+ <a href="https://www.lptmc.jussieu.fr/users/lesne">Annick Lesne</a>, LPTMC (Sorbonne Université) et IGMM (Montpellier)<br/>
+ </p>
+
+
+ <h3>Programme</h3> (préliminaire)
+
+ </p>
+ <p align="left">
+ <b>09h00 - 09h30</b> - <b>Accueil</b><br/>
+ <b>09h30 - 09h35</b> - <b>Introduction des journées</b><br/>
+ <b>09h35 - 10h20</b> - <b>Conférence plénière</b>. Gregory Nuel. <i>Estimating causal effects in gene regulation networks.</i><br/>
+ <b>10h20 - 10h45</b> - Jérémie Pardo. <i>Sequential reprogramming of biological network fate</i>.<br/>
+ <b>10h45 - 11h10</b> - Aurélien Desoeuvre. <i>Homeostasis by interval</i>.<br/>
+ <b>11h10 - 11h35</b> - Aurélien Naldi. <i>Dynamic modeling of cell populations with UPMaBoSS.</i><br/>
+ <b>11h35 - 12h00</b> - Stephen Chapman. <i>Flux balance analysis reveals acetate metabolism modulates cyclic electron flow and alternative glycolytic pathways in Chlamydomonas reinhardtii.</i><br/>
+ <b>12h00 - 12h10</b> - Flash talks. <br/>
+ <b>12h10 - 13h30</b> - <b>Pause déjeuner</b><br/>
+ <b>13h30 - 14h15</b> - <b>Conférence plénière</b>. Annick Lesne. <i>Bifurcation analysis of biological circuits: time scales matter</i>.<br/>
+ <b>14h15 - 14h40</b> - Diane Peurichard. <i>A new model for the emergence of vascular networks.</i><br/>
+ <b>14h40 - 15h05</b> - D. Regnault. <i>Non-cooperatively assembling large structures.</i> <br/>
+ <b>15h05 - 15h35</b> - <b>Pause</b><br/>
+ <b>15h35 - 16h00</b> - Émilie Allart. <i>Computing Difference Abstractions of Metabolic Networks.</i><br/>
+ <b>16h00 - 16h25</b> - Andreea Beica. <i>Tropical abstractions of Biochemical Reaction Networks with guarantees.</i><br/>
+ <b>16h25 - 16h50</b> - Zach Fox. <i>Optimal Experiment Designs of Signal Activated Stochastic Gene Expression in S. Cerevisae.</i><br/>
+ <b>16h50 - 17h15</b> - Mathilde Koch.<i>Large scale active-learning-guided exploration to maximize cell-free production</i>.<br/>
+ </p>
+
+ <h3>Résumés</h3>
+ <p align="justify">
+ <b> Émilie Allart</b> - <i>Computing Difference Abstractions of Metabolic Networks</i>.</br>
+ Algorithms based on abstract interpretation were proposed recently for predicting changes of reaction networks with partial kinetic information. Their prediction precision, however, depends heavily on which heuristics are applied in order to add linear consequences of the steady state equations of the metabolic network. In this paper we ask the question whether such heuristics can be avoided while obtaining the highest possible precision. This leads us to the first algorithm for computing the difference abstractions of a linear equation system exactly without any approximation. This algorithm relies on the usage of elementary flux modes in a nontrivial manner, first-order definitions of the abstractions, and finite domain constraint solving.</br><br/>
+
+ <b>Andreea Beica</b> - <i>Tropical abstractions of Biochemical Reaction Networks with guarantees.</i><br/>
+ Biochemical molecules interact through modification and binding reactions, giving raise to a combinatorial number of possible biochemical species. The time-dependent evolution of concentrations of the species is commonly described by a system of coupled ordinary differential equations (ODEs). However, the analysis of such high-dimensional, non-linear system of equations is often computationally expensive and even prohibitive in practice. The major challenge towards reducing such models is providing the guarantees as to how the solution of the reduced model relates to that of the original model, while avoiding to solve the original model. In this paper, we have designed and tested an approximation method for ODE models of biochemical reaction systems, in which the guarantees are our major requirement. Borrowing from tropical analysis techniques, dominance relations among terms of each species' ODE are exploited to simplify the original model, by neglecting the dominated terms. As the dominant subsystems can change during the system's dynamics, depending on which species dominate the others, several possible modes exist. Thus, simpler models consisting of only the dominant subsystems can be assembled into hybrid, piecewise smooth models, which approximate the behavior of the initial system. By combining the detection of dominated terms with symbolic bounds propagation, we show how to approximate the original model by an assembly of simpler models, consisting in ordinary differential equations that provide time-dependent lower and upper bounds for the concentrations of the initial models species. Our method provides sound interval bounds for the concentrations of the chemical species, and hence can serve to evaluate the faithfulness of tropicalization-based reduction heuristics for ODE models of biochemical reduction systems. The method is tested on several case studies.<br/><br/>
+
+ <b>Stephen Chapman</b> - <i>Flux balance analysis reveals acetate metabolism modulates cyclic electron flow and alternative glycolytic pathways in Chlamydomonas reinhardtii.</i><br/>
+ Cells of the green alga Chlamydomonas reinhardtii cultured in the presence of acetate perform mixotrophic growth, involving both photosynthesis and organic carbon assimilation. Under such conditions, cells exhibit a reduced capacity for photosynthesis but a higher growth rate, compared to phototrophic cultures. Better understanding of the down regulation of photosynthesis would enable more efficient conversion of carbon into valuable products like biofuels. In this study, Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) have been used with a genome scale model of C. reinhardtii to examine changes in intracellular flux distribution in order to explain their changing physiology. Additionally, a reaction essentiality analysis was performed to identify which reaction subsets are essential for a given growth condition. Our results suggest that exogenous acetate feeds into a modified tricarboxylic acid (TCA) cycle, which bypasses the CO2 evolution steps, explaining increases in biomass, consistent with experimental data. In addition, reactions of the oxidative pentose phosphate and glycolysis pathways, inactive under phototrophic conditions, show substantial flux under mixotrophic conditions. Importantly, acetate addition leads to an increased flux through cyclic electron flow (CEF), but results in a repression of CO2 fixation via Rubisco, explaining the down regulation of photosynthesis. However, although CEF enhances growth on acetate, it is not essential-impairment of CEF results in alternative metabolic pathways being increased. We have demonstrated how the reactions of photosynthesis interconnect with carbon metabolism on a global scale, and how systems approaches play a viable tool in understanding complex relationships at the scale of the organism. <br/><br/>
+
+ <b>Aurélien Desoeuvre</b> - <i>Homeostasis by interval</i>.<br/>
+ The presence of parametric uncertainty in biological systems and the importance
+ of the homeostasis concept in medicine led us to look for a method to find
+ homeostatic variables of a system. To do this, we use an algorithmic method
+ based on the Ibex library (Interval Based EXplorer)(Constraint programming),
+ and a definition of homeostasis on a equilibrium in terms of intervals.<br/><br/>
+
+ <b>Zachary R Fox</b> - <i>Optimal Experiment Designs of Signal Activated Stochastic Gene Expression in S. Cerevisae.</i> <br/>
+ Modern biological experiments are complex and gaining quantitative insight from data collected by such experiments remains a challenge.
+ Increasingly, computational models of complex stochastic biological systems are used as a method to understand how a particular system works and also to make quantitative predictions about how the system will behave under different conditions.
+ Quantitative predictions allow one to use models to design experiments for particular goals, such as learning about model parameters.
+ A classic approach to experiment design is to use Fisher information, which quantifies the expected information a particular experiment will reveal about model parameters.
+ The Finite State Projection based Fisher information was recently developed and allows one to compute the Fisher information for these systems without resorting to moment-based computations of the master equation dynamics.
+ In this work, we use a previously validated stochastic model of stress response genes in _S. cerevisae_ to design optimal measurements of mRNA.
+ We validate the Fisher information for a time-varying stochastic model in the context of the chemical master equation.
+ We then optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters.
+ We extend the Fisher information approach to design experiments which minimize the uncertainty in the extracellular environment - in this case, in the extracellular salinity.
+ This work demonstrates the potential of quantitative models to make sense of modern biological data sets and close the loop between data collection and quantitative modeling.<br/><br/>
+
+<b>Mathilde Koch</b>.<i>Large scale active-learning-guided exploration to maximize cell-free production.</i><br/>
+Cell-free systems are an increasingly mature and useful platform for prototyping, testing, and engineering biological parts and systems. However, lysate-based cell-free systems currently suffer from important batch-to-batch variability which render quantitative predictions and mathematical modeling hard to generalise between set-ups. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality. <br/><br/>
+
+ <b>Annick Lesne</b>. <i>Bifurcation analysis of biological circuits: time scales matter</i>.<br/>
+ <i>Joint work with Marc-Thorsten Hütt (Jacobs University, Bremen, Germany) and his former students Pencho Yordanov and Stefka Tyanova.</i><br/>
+ The core of dynamical systems theory is to focus on asymptotic states of the system and to investigate its phase portrait and its bifurcation diagram. However, when considering complex biological systems, this approach could fail, for instance when investigating the effect of external stimuli on a system displaying several characteristic time scales. I will present a case study of a system comprising two interlinked positive feedback loops. Depending on the time scales of these loops, the system could (or not) be robust with respect to external noise. When a stimulus with an intermediary time scale is applied, incomplete bifurcation and stabilization of non-equilibrium states are observed. I will conclude with some other examples where the current framework of bifurcation theory is not sufficient to capture the complexity of the dynamics.<br/><br/>
+
+ <b>P.-E. Meunier and D. Regnault</b> - <i>Non-cooperatively assembling large structures.</i><br/>
+ Algorithmic self-assembly is the study of the local, distributed, asynchronous algorithms ran by molecules to self-organise, in particular during crystal growth. The general cooperative model, also called ``temperature 2", uses synchronisation to simulate Turing machines, build shapes using the smallest possible amount of tile types, and other algorithmic tasks.
+ However, in the non-cooperative (``temperature 1") model, the growth process is entirely asynchronous, and mostly relies on geometry. Even though the model looks like a generalisation of finite automata to two dimensions, its 3D generalisation is capable of performing arbitrary (Turing) computation, and of universal simulations, whereby a single 3D non-cooperative tileset can simulate the dynamics of all possible 3D non-cooperative systems, up to a constant scaling factor.
+ However, the original 2D non-cooperative model is not capable of universal simulations, and the question of its computational power is still widely open and it is conjectured to be weaker than ``temperature 2" or its 3D counterpart. Here, we show an unexpected result, namely that this model can reliably grow assemblies of diameter n log(n) with only n tile types, which is the first asymptotically efficient positive construction.<br/><br/>
+
+ <b>Aurélien Naldi</b>. <i>Dynamic modeling of cell populations with UPMaBoSS</i>.<br/>
+ Joint work with: Gautier Stoll (CRC, Paris), Vincent Noel (Curie, Paris), Eric Viara (Sysra), Emmanuel Barillot (Curie, Paris), Denis Thieffry (IBENS Paris), Laurence Calzone (Curie, Paris)<br/>
+ Over the last decade, various parts of the immune response have been studied through
+ qualitative dynamical models. These models focus on intra-cellular mechanisms, often
+ controlled by external events and leading to alternative cell fates. However, they do not
+ fully account for the control of the size and composition of the cell population, which
+ ultimately determines the nature and intensity of the immune response.<br>
+ In this context, UPMaBoSS is a new framework for dynamical modeling of circulating cell
+ populations, based on qualitative models of the intra-cellular mechanisms. It relies on the
+ pre-existing tool MaBoSS, which estimates a distribution of probabilities of individual cellular
+ states. Here, we propose to interpret this distribution of probabilities as a mirror of the
+ composition of an heterogeneous cell population. UPMaBoSS enables this interpretation by
+ accounting for inter-cellular communication, cell division and cell death. It provides an efficient
+ and natural method for extending mechanistic models toward the population scale.<br/>
+ Preliminary studies on models of immune cells confirm that accounting for the population-level feedbacks can change drastically the results, in particular in the balance between sub-populations of regulatory T cells.<br/><br/>
+
+ Gregory Nuel. <i>Estimating causal effects in gene regulation networks.</i><br/>
+ In this talk we present a modelization of gene regulation networks based on causal Gaussian Bayesian networks. In the presence of any arbitrary mixture of observation (e.g. wild type experiments) and intervention data (e.g. knock-out experiments), we establish the maximum likelihood estimator given the directed acyclic graph (DAG) structure as a simple linear regressor. In a second time, we then use the Metropolis-Hasting algorithm jointly with a model selection criterion (e.g. BIC) to obtain the full posterior distribution of DAG structures and parameters. This collection of Bayesian networks allows to estimate direct and total causal effects. Finally, we present a DAG clustering algorithm that might be useful for interpreting and representing the posterior DAG distribution.<br/><br/>
+
+ <b>Jérémie Pardo</b> - <i>Sequential reprogramming of biological network fate.</i><br/>
+Cell reprogramming consists in modifying gene expression to induce a particular cell behavior naturally or artificially. A number of potential beneficial outcomes in the field of médecine such as cancerous targeted therapy, regenerative or precision medicine could come from such reprogramming. The action of targeted therapies can be interpreted as network rewiring as the effect of mutations and drugs can be described as elementary topological actions on the network, assimilated to a control. The main issue is to infer the control inputs (i.e. topological actions) redirecting the biological system dynamics to an expected fate. Two computational approaches of controls can be studied: single control or sequential control of the interaction network. In this talk, we will present a framework investigating the sequential control of Boolean controlled networks. Control sequence Inference is a decision problem of PSPACE complexity. Thus, in the aim to find a minimal parsimonious control sequence, we propose a heuristic method focused on the partitioning of the states dependent on observed variables and an abductive-based inference.<br/><br/>
+
+ <b>Diane Peurichard</b> - <i>A new model for the emergence of vascular networks.</i>
+ Abstract: The generation of vascular networks is a long standing problem which has been the subject of intense research in the past decades, because of its wide range of applications (tissue regeneration, wound healing, cancer treatments etc). The mechanisms involved in the formations of vascular networks are complex and despite the vast amount of research devoted to it there are still many mechanisms involved which are poorly understood. Our aim is to bring insight into the study of vascular networks by defining heuristic rules, as simple as possible, and to simulate them numerically to test their relevance in the vascularization process. We introduce a hybrid agent-based/continuum model coupling blood flow, oxygen flow, capillary network dynamics and tissues dynamics. We provide two different, biologically relevant geometrical settings and numerically analyze the influence of each of the capillary creation mechanism in detail. All mechanisms seem to concur towards a harmonious network but the most important ones are those involving oxygen gradient and sheer stress.<br/><br/>
+ </p>
+
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
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+<!DOCTYPE HTML>
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+ <title>Bioss</title>
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+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
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+ <!-- insert the page content here -->
+ <h1>Workshop "Metabolism in Systems Biology"</h1>
+
+ <p align="justify">
+ Dates : 2016 Nov. 24th, 10:00 -- Nov. 25th 16:00
+ </p>
+ <p align="justify">
+ Place :
+ <a href="http://www.univ-lille1.fr">Lille University</a>,
+ Cité scientifique,
+ Villeneuve d'Ascq.
+ </p>
+ <p align="justify">
+ Organization :<br/>
+ <a href="http://www.cristal.univ-lille.fr/BioComputing">BioComputing
+ group</a> of the computer science
+ lab <a href="http://cristal.univ-lille.fr">CRIStAL</a> of the
+ university of Lille
+ <br/>
+ (<a href="http://cristal.univ-lille.fr/~lhoussai">Cedric
+ Lhoussaine</a>, <a href="http://researchers.lille.inria.fr/~niehren">Joachim
+ Niehren</a>
+ and <a href="http://www.cristal.univ-lille.fr/~versaric">Cristian Versari</a>)
+ </p>
+
+ <h3>Topic of the workshop:</h3>
+ <p align="justify">
+ Given that metabolism is a central aspect of all living systems, the domain of
+ metabolomics has become a large research domain in molecular biology. In the
+ last 10 years, this has raised increasing interest in the formal modeling and
+ analysis of metabolic networks. A typical example is flux balance analysis, but
+ also the control of the metabolic fluxes has become subject to formal modeling
+ and prediction approaches. The objective of the workshop is therefore to bring
+ together researchers from computer science, biology, chemistry, and mathematics,
+ who are working on different aspects of the modeling of metabolic networks.
+ </p>
+
+ <article>
+ <header>
+ <h4>A model of the BCAA metabolic network of B.~Subtilis</h4>
+ </header>
+ <img src="leucine.png" alt="Leucine network.">
+
+ </article>
+
+ Bioss will support the workshop fees and the hotel costs. Only the
+ travel costs are left to the participants. If you are interested to
+ join the workshop, please contact the organizers by Email to
+ <a href="mailto:bio-computing-metabolism@lists.gforge.inria.fr">
+ bio-computing-metabolism@lists.gforge.inria.fr
+ </a>
+
+ <h3>Program</h3>
+ <i>The precise schedule is yet to be announced but we will be
+ starting at 10:00 on Nov. 24th and finish at 16:00 on Nov. 25th.</i>
+ <ul>
+ <li>
+ <a href="http://teusinklab.nl/frank-bruggeman">Frank Brueggeman</a> TBA
+ </li>
+ <li>
+ <a href="http://www-sop.inria.fr/members/Olivier.Bernard/OBernard-fra.html">Olivier Bernard</a> Dynamic metabolic modelling of microalgae. Also presents work of <a href="https://www.researchgate.net/profile/Caroline_Baroukh">Caroline Baroukh</a>.
+ </li>
+ <li>
+ <a href="https://www.researchgate.net/profile/Francois_Coutte">François Coutte</a> Surfactine overproduction and how the models for metabolism of B.Subtilis. Joint
+ work with <a href="http://www.cristal.univ-lille.fr/~versaric">Cristian Versari</a>, <a href="http://researchers.lille.inria.fr/~niehren">Joachim Niehren</a>, Philippe Jacques, and Debarun Dali.
+ </li>
+ <li>
+ <a href="https://lbbe.univ-lyon1.fr/-Kahn-Daniel-.html?lang=fr">Daniel Kahn</a> Discussing metabolic interpretation of metagenomic data.
+ </li>
+ <li>
+ <a href="http://researchers.lille.inria.fr/~niehren">Joachim Niehren</a> Structural simplification of reaction networks in partial steady states. Joint work
+ with <a href="">Guillaume Madelaine</a>, <a href="http://www.cristal.univ-lille.fr/~lhoussai">Cédric Lhoussaine</a>, and Elisa Tonello.
+ </li>
+ <li>
+ <a href="http://jean-pierre.mazat.pagesperso-orange.fr">Jean-Pierre Mazat</a> Small is beautiful. Human scale metabolic model(s).
+ </li>
+ <li>
+ <a href="https://www.linkedin.com/in/nathalie-poupin-81045455">Nathalie Poupin</a> Exploring metabolic modulations using genome scale network modelling and omics data.
+
+ </li>
+ <li>
+ <a href="https://www.lri.fr/~speres">Sabine Peres</a> and <a href="https://www.lri.fr/~dague">Philippe Dague</a> SAT-based approaches for metabolic pathway analysis.
+ </li>
+ <li>
+ <a href="http://www.irisa.fr/dyliss/anne.siegel">Anne Siegel</a> Reconstruction d'un réseau métabolique à partir de données brutes.
+ </li>
+ <li>
+ <a href="https://www.researchgate.net/profile/Moritz_Von_Stosch">Moritz von Stosch</a> An elementary mode constrained latent variable method for reaction flux data analysis.
+
+ </li>
+ <li>
+ <a href="http://www.i3s.unice.fr/~khoodeer/">Rajeev Khoodeeram</a> A formal model of metabolism to decipher Crabtree effect. Joint work with
+ <a href="http://www.supbiotech.fr/professeurs-biotechnologie.aspx">Jean-Yves Trosset</a> and Gilles Bernot.
+ </li>
+ <li>
+ <a href="">Emilie Allart</a> Change prediction of reaction networks with partial kinetic information and the elementary
+ modes. Joint work with <a href="http://www.cristal.univ-lille.fr/~versaric">Cristian Versari</a> and <a href="http://researchers.lille.inria.fr/~niehren">Joachim Niehren</a>.
+ </li>
+ </ul>
+ <!-- h4>Thursday 24th November 2016</h4>-->
+ <!-- p align="left">
+ <b>10h15 - 10h30</b> -
+ <a href="siegel.pdf">slides</a><br>
+ Anne Siegel - <i>Présentation du GT
+ Bioss</i><br>
+ <b>10h30 - 11h15</b> -
+ <a href="feret.pdf">slides</a><br>
+ Jérôme Feret - <i>Réduction de modèles de
+ voies de signalisation intracellulaires</i><br>
+ <b>11h15 - 12h00</b> -
+ <a href="radulescu.pdf">slides</a><br>
+ Ovidiu Radulescu - <i>Taming the complexity of
+ biochemical networks through model reduction and tropical
+ geometry</i><br>
+ <b>14h00 - 14h45</b> -
+ <a href="richard.pdf">slides</a><br>
+ Adrien Richard - <i>Reduction of finite
+ dynamical systems and linear network coding solvability</i><br>
+ <b>14h45 - 15h30</b> -
+ <a href="eveillard.pdf">slides</a><br>
+ Damien Éveillard - <i>Rechercher des modules
+ dans les réseaux métaboliques</i><br>
+ <b>16h00 - 16h45</b> -
+ <a href="madelaine.pdf">slides</a><br>
+ Guillaume Madelaine - <i>Structural
+ simplification of chemical reaction networks preserving deterministic
+ semantics</i><br>
+ <b>16h45 - 17h30</b> -
+ <a href="basso.pdf">slides</a><br>
+ Adrien Basso-Blandin - <i>Modèle de
+ représentation de connaissances annoté pour la biologie</i><br>
+ <b>17h30 - 18h15</b> -
+ <a href="pauleve.pdf">slides</a><br>
+ Loïc Paulevé - <i>Goal-oriented reduction of
+ automata networks</i><br>
+ </p -->
+
+ <!-- h4>Friday 25th November 2016</h4 -->
+ <!-- p align="left">
+ <b>09h00 - 09h45</b> -
+ <a href="fages.pdf">slides</a><br>
+ François Fages - <i>Réductions de modèles par
+ épimorphismes de sous-graphes</i><br>
+ <b>09h45 - 10h30</b> -
+ <a href="delaplace.pdf">slides</a><br>
+ Franck Delaplace - <i>Analogous dynamics of
+ Boolean networks</i><br>
+ <b>11h00 - 11h45</b> -
+ <a href="naldi.pdf">slides</a><br>
+ Aurélien Naldi - <i>Une méthode de réduction
+ pour la manipulation de grands modèles logiques</i><br>
+ <b>11h45 - 12h30</b> -
+ <a href="abou_jaoude.pdf">slides</a><br>
+ Wassim Abou-Jaoudé - <i>Derivation of dynamical
+ qualitative models from biochemical networks</i><br>
+ <b>14h00 - 14h45</b> -
+ <a href="melliti.pdf">slides</a><br>
+ Tarek Melliti - <i>Analysis of modular
+ organisation of interaction networks based on asymptotic
+ dynamics</i><br>
+ <b>14h45 - 15h30</b> -
+ <a href="tournier.pdf">slides</a><br>
+ Laurent Tournier - <i>Uncovering regulations in
+ B. subtilis metabolic network, combining optimal resource allocation
+ and Boolean inference</i><br>
+ </p -->
+
+ <!-- h3>Summary</h3>
+ <p align="justify">
+ <b>Wassim Abou-Jaoudé</b> - <i>Derivation of dynamical qualitative
+ models from biochemical networks</i><br>
+ As technological advances allow a better identification of cellular
+ networks, more and more molecular data are produced allowing the
+ construction of detailed molecular interaction maps. One strategy to
+ get insights into the dynamical properties of such systems is to
+ derive compact dynamical models from these maps, which would then be
+ handled more efficiently for the analysis of their dynamics.
+ Starting from two specific case studies of networks, I will present a
+ methodology for the derivation of qualitative dynamical models from
+ biochemical networks. Properties are formalised using abstraction
+ interpretation techniques. We first abstract states and traces by
+ quotienting the state space by intervals. The induced abstract
+ semantics is too coarse to reproduce the properties of interest for
+ our two examples. We then refine the abstract semantics by introducing
+ additional constraints and information on the kinetics computed by
+ abstract interpretation. The resulting semantics is able to reproduce
+ our properties of interest.<br><br>
+ <b>Adrien Basso-Blandin</b> - <i>Modèle de représentation de
+ connaissances annoté pour la biologie</i><br>
+ L'étude des voies de signalisations biologiques des cancers est un
+ travail extrêmement complexe. En effet, de tels systèmes possèdent de
+ nombreux paramètres, agents et processus, mais ils sont étudiés par
+ fragments et leurs littératures et données sont fragmentées,
+ distribuées et parfois contradictoires. Il est difficile de construire
+ les modèles complets, explicatifs de tels systèmes complexes et des
+ interactions dans ces systèmes qui sont provoquées par beaucoup de
+ facteurs interagissants de manière peu connue ou mal comprise.
+ Les "Big mechanisms" sont de grands modèles explicatifs de systèmes
+ complexes au sein desquels les interactions ont des effets causals
+ importants. La collection de données de masse est de plus en plus
+ automatisée, néanmoins, la création de "Big mechanisms" reste un
+ processus manuel de plus en plus difficile à réaliser de par la
+ fragmentation et la distribution de connaissances. L'automatisation de
+ la conception de ces "Big mechanisms" permettrait une évolution
+ majeure pour la science et la façon dont la recherche est
+ réalisée.
+ Ici nous introduisons d'un coté un modèle de représentation de
+ connaissance pour la biologie afin d'intégrer plus ou moins
+ immédiatement et automatiquement (ou "semi automatiquement") au sein
+ de modèles causals explicatifs, des connaissances biologiques
+ extraites de la littérature. Combiné à ce modèle, nous proposons une
+ traduction automatique de cette représentation de connaissances en
+ modèles Kappa.<br><br>
+ <b>Franck Delaplace</b> - <i>Analogous dynamics of Boolean
+ networks</i><br>
+ Different Boolean networks may reveal similar dynamics although their
+ definition differs, then preventing their distinction from the
+ observations. This raises the question about the sufficiency of a
+ particular Boolean network for properly reproducing a modeled
+ phenomenon to make realistic predictions. The question actually
+ depends on the invariant properties of behaviorally similar Boolean
+ networks. We address this issue by considering that the similarity is
+ formalized by isomorphism on graphs modeling their dynamics. The
+ similarity also depends on the parameter governing the updating
+ policy, called the "mode". We define a general characterization of the
+ group of isomorphism preserving the mode. From this characterization,
+ we deduce invariant structural properties of the interaction graph and
+ conditions to maintain an equivalence through mode variation.<br><br>
+ <b>Damien Éveillard</b> - <i>Rechercher des modules dans les réseaux
+ métaboliques</i><br>
+ Les récents progrès biotechnologiques permettent de reconstruire
+ des réseaux métaboliques à l’échelle des génomes pour notamment
+ appliquer les approches de type FBA. Cependant, au delà de l’intérêt
+ de ces approches pour prédire des comportements quantitatifs,
+ l’analyse per se des réseaux métaboliques reste difficile. Cette
+ difficulté est particulièrement d’actualité pour analyser le réseau
+ métabolique qui résultent d’interactions microbiennes, et qui mettent
+ en oeuvre différents réseaux métaboliques issus des espèces
+ bactériennes en présence. Cet exposé propose de surmonter cette
+ difficulté avec la recherche des modules de flux. Cette technique
+ analyse le réseau métabolique en décomposant l’espace de solutions
+ optimales. Nous montrerons, après application sur deux systèmes de
+ référence, que cette décomposition est biologiquement intéressante
+ biologique et qu’elle ouvre de riches perspectives méthodologiques
+ pour la modélisation des réseaux métaboliques.<br><br>
+ <b>François Fages</b> - <i>Réductions de modèles par épimorphismes de
+ sous-graphes</i><br>
+ Nous proposons un cadre de réduction de modèles, basé uniquement sur
+ des graphes, qui permet d'organiser les bases de modèles en un ordre
+ partiel. Pour capturer le processus de réduction lui-même, nous
+ utilisons un type particulier de morphismes de graphes: les
+ épimorphismes de sous-graphes, qui permettent la fusion et
+ l'effacement de sommets. Nous commencerons en analysant l'ordre
+ partiel qui émerge des opérations de fusion et d'effacement,
+ montrerons sa complexité théorique et sa résolution pratique en
+ programmation par contraintes, et évaluerons les performances et la
+ précision de cette approche sur la base de modèles BioModels. Enfin,
+ nous discuterons de nos travaux en cours sur la recherche de motifs
+ dans les réseaux de réactions protéiques, ainsi que sur la prise en
+ compte des critères dynamiques par des méthodes d'algèbre
+ tropicale.<br><br>
+ <b>Jérôme Feret</b> - <i>Réduction de modèles de voies de
+ signalisation intracellulaire</i><br>
+ Les voies de signalisation intracellulaire sont des cascades
+ d'interaction entre protéines, qui permettent à la cellule de recevoir
+ des signaux, de les propager jusqu'à son noyau, puis de les intégrer,
+ ce qui, in fine, influe sur le comportement global de la cellule. Les
+ protéines s'associent entre elles sur des sites de liaisons, puis
+ modifient la structure spatiale de leurs voisines, ce qui a pour effet
+ de cacher ou de découvrir leurs autres sites de liaisons, et donc
+ d'empêcher ou de faciliter d'autres interactions. De vastes bases de
+ données ont été conçues pour répertorier les différentes interactions
+ connues entre les sites des protéines. Cependant, nous ne savons
+ toujours pas clairement comment les propriétés physiologiques de la
+ cellule émergent de ces interactions. La difficulté principale est la
+ grande combinatoire de ces modèles. En effet, chaque protéine a
+ beaucoup de sites de liaisons. Ainsi, un très grand nombre de
+ complexes biomoléculaires différents peut se former. Pour décrire ces
+ modèles, nous proposons d'utiliser des graphes pour la représentation
+ des complexes biomoléculaires et des règles de réécritures pour la
+ spécification des interactions entre les protéines. En particulier,
+ ces règles sont contextuelles : elles décrivent non seulement les
+ transformations sur les complexes biomoléculaires, mais aussi les
+ conditions nécessaires à ces transformations. Ceci offre une
+ représentation très compacte et pratique d'un modèle. Par ailleurs ces
+ règles permettent de formaliser le comportement des modèles à
+ différents niveaux d'abstraction (qualitatifs ou quantitatifs).
+ Malheureusement, l'écueil de la complexité combinatoire refait surface
+ lorsque l'on cherche à calculer de manière effective ce
+ comportement.<br>
+ Nous proposons une méthode pour réduire la taille des systèmes
+ différentiels qui décrivent le comportement de ces modèles. Nous
+ utilisons une analyse du flot d'information entre les différents sites
+ des complexes biomoléculaires. Ainsi, pour chaque site de liaison
+ d'un complexe biomoléculaire, nous détectons quelles sont les parties
+ de ce complexe qui peuvent influencer la capacité de lier ou de délier
+ ce site. Nous en déduisons des paires de sites dont on peut abstraire
+ la relation entre l'état de liaison, car les ensembles de sites
+ qu'ils peuvent influencer sont disjoints. Cela nous permet de découper
+ les espèces biomoléculaires en plus petits morceaux (en séparant de
+ telles paires de sites). Nous obtenons ainsi un système différentiel
+ portant sur la concentration de ces morceaux de complexes
+ biomoléculaires, qui sont beaucoup moins nombreux que les complexes
+ biomoléculaires du système différentiel du modèle initial, et ce sans
+ jamais avoir écrit explicitement ce système initial. Pourtant, notre
+ méthode de réduction est exacte : nous avons la preuve que la solution
+ du système obtenu, est la projection exacte de la solution du système
+ initial.<br><br>
+ <b>Guillaume Madelaine</b> - <i>Structural simplification of chemical
+ reaction networks preserving deterministic semantics</i><br>
+ We study the structural simplification of chemical reaction networks
+ preserving the deterministic kinetics. We aim at finding
+ simplification rules that can eliminate intermediate molecules while
+ preserving the dynamics of all others. The rules should be valid even
+ though the network is plugged into a bigger context. An example is
+ Michaelis-Menten’s simplification rule for enzymatic reactions. In
+ this paper, we present a large class of structural simplifications
+ rules for reaction networks that can eliminate intermediate molecules
+ at equilibrium, without assuming that all molecules are at
+ equilibrium, i.e. in a steady state. We prove the correctness of our
+ simplification rules for all contexts that preserve the equilibrium of
+ the eliminated molecule. Finally, we illustrate at a concrete example
+ networks from systems biology, that our simplification rules may allow
+ to drastically reduce the size of reaction networks in
+ practice.<br><br>
+ <b>Tarek Melliti</b> - <i>Analysis of modular organisation of
+ interaction networks based on asymptotic dynamics</i><br>
+ We will present a work related to modularity in biological interaction
+ networks, for which has been developped a discrete theoretical
+ framework based on the analysis of the asymptotic dynamics of
+ biological interaction networks. More precisely, we will exhibit
+ formal conditions under which agents of interaction networks can be
+ grouped into modules, forming a modular organisation. We will see that
+ the conventional decomposition into strongly connected components
+ fulfills the formal conditions of being a modular organisation. We
+ will also propose a modular and incremental algorithm for an
+ efficient equilibria computation. Furthermore, we will point out that
+ our framework enables a finer analysis providing a decomposition into
+ elementary modules, possibly smaller than strongly connected
+ components.<br><br>
+ <b>Aurélien Naldi</b> - <i>Une méthode de réduction pour la
+ manipulation de grands modèles logiques</i><br>
+ Nous avons proposé une méthode de réduction de modèles logiques basée
+ sur l'élimination de composants (sélectionnés manuellement) en
+ réécrivant les fonctions logiques de leurs cibles. L'impact de cette
+ réduction sur le comportement dynamique dans le cadre asynchrone est
+ bien défini, en particulier elle conserve les attracteurs du système
+ complet, mais peut dans certaines conditions en créer de nouveaux ou
+ affecter leur atteignabilité. Après avoir introduit la méthode, je
+ discuterai de critères de sélection des composants à éliminer afin de
+ mieux préserver la dynamique, ainsi que de liens entre cette méthode
+ de réduction et d'autres approches d'analyse statique.<br><br>
+ <b>Loïc Paulevé</b> - <i>Goal-oriented reduction of automata
+ networks</i><br>
+ I'll present on-going results on the reduction of automata networks
+ dedicated to a given reachability goal (e.g., activation of a
+ particular component). Based on previous work on abstract
+ interpretation of traces in automata networks, I'll show that we can
+ identify transitions that are useless for reaching a given goal state.
+ At the end, the reduction produces an automata network that can
+ generate all the minimal traces leading to the given goal, while
+ significantly shrinking its global dynamics.<br><br>
+ <b>Ovidiu Radulescu</b> - <i>Taming the complexity of biochemical
+ networks through model reduction and tropical geometry</i><br>
+ Biochemical networks are used as models of cellular physiology with
+ diverse applications in biology and medicine. In the absence of
+ objective criteria to detect essential features and prune secondary
+ details, networks generated from data are too big and therefore out of
+ the applicability of many mathematical tools for studying their
+ dynamics and behavior under perturbations. However, under
+ circumstances that we can generically denote by multi-scaleness,
+ large biochemical networks can be approximated by smaller and simpler
+ networks. Model reduction is a way to find these simpler models that
+ can be more easily analyzed. We discuss several model reduction
+ methods for biochemical networks with polynomial or rational rate
+ functions and propose as their common denominator the notion of
+ tropical equilibration, meaning finite intersection of tropical
+ varieties in algebraic geometry. Using tropical methods, one can
+ strongly reduce the number of variables and parameters of biochemical
+ network. For multi-scale networks, these reductions are computed
+ symbolically on orders of magnitudes of parameters and variables, and
+ are valid in wide domains of parameter and phase spaces.<br><br>
+ <b>Adrien Richard</b> - <i>Reduction of finite dynamical systems and
+ linear network coding solvability</i><br>
+ Linear network coding transmits data through networks by letting the
+ intermediate nodes combine the messages they receive and forward the
+ combinations towards their destinations. The solvability problem asks
+ whether the demands of all the destinations can be simultaneously
+ satisfied by using linear network coding. This problem can be
+ formulated in terms of fixed points finite dynamical systems, which
+ are usually called discrete networks, or Boolean networks when all the
+ variables are binary variables.<br>
+ Naldi, Remy, Thieffry and Chaouiya (TCS 2011) introduced technics for
+ removing some variables in a finite dynamical system without changing
+ the number of fixed points. In this presentation, we show that this
+ reduction technics can be used to obtain new results on the linear
+ network coding solvability problem.<br>
+ We first show that triangle-free undirected graphs are linearly
+ solvable if and only if they are solvable by routing (this is the
+ first classification result for the linear network coding solvability
+ problem). Then, we exhibit a new class of non-linearly solvable
+ graphs. Finally, we determine large classes of strictly linearly
+ solvable graphs.<br><br>
+ <b>Laurent Tournier</b> - <i>Uncovering regulations in B. subtilis
+ metabolic network, combining optimal resource allocation and
+ Boolean inference</i><br>
+ We previously developed and validated the constraint-based modeling
+ method "Resource Balance Analysis" (RBA) that accurately predicts
+ resource allocation (i.e. growth rate, protein allocation, metabolic
+ configuration) in the model bacterium Bacillus subtilis for a wide
+ range of growth conditions. RBA is able to predict induced/repressed
+ subsystems in the metabolic network, thus mimicking the
+ repression/activation of metabolic pathways by a genetic regulator.
+ The question is now to explore systematically the relation between
+ predicted metabolic configurations and simulated medium composition:
+ (a) to determine a rule of activation of the encoding gene and (b) to
+ determine if the inferred rule coincides with the known biological
+ regulation of the gene. To explore the exponential number of
+ predicted metabolic configurations, we propose to use Boolean
+ inference. In particular, we propose a method to infer monotone
+ (unate) Boolean functions on a minimal support. Applied to the central
+ carbon metabolism of B. subtilis, first results are encouraging as the
+ method predicts most of the regulations as they are known today.
+ </p -->
+
+ <h3>Participants</h3>
+ <p align="justify">
+ <a href="http://teusinklab.nl/frank-bruggeman">Frank Brueggeman</a> Vrije Universitat Amsterdam <br/>
+ <a href="https://www.researchgate.net/profile/Caroline_Baroukh">Caroline Baroukh</a> Université de Toulouse <br/>
+ <a href="http://www-sop.inria.fr/members/Olivier.Bernard/OBernard-fra.html">Olivier Bernard</a> Inria Sophia-Antipolis <br/>
+ <a href="https://www.lri.fr/~dague">Philippe Dague</a> Université
+ Paris Sud <br/>
+ <a href="https://lbbe.univ-lyon1.fr/-Kahn-Daniel-.html?lang=fr">Daniel
+ Kahn</a> INRA Lyon <br/>
+ <a href="http://www.i3s.unice.fr/~khoodeer/">Rajeev KHOODEERAM</a> I3S, UNICE<br/>
+ <a href="http://www.cristal.univ-lille.fr/~lhoussai">Cédric Lhoussaine</a> Université de Lille<br/>
+ <a href="http://jean-pierre.mazat.pagesperso-orange.fr">Jean-Pierre Mazat</a> Université Bordeaux<br/>
+ <a href="http://researchers.lille.inria.fr/~niehren">Joachim Niehren</a> Inria Lille <br/>
+ <a href="https://www.linkedin.com/in/nathalie-poupin-81045455">Nathalie Poupin</a> INRA Toulouse <br/>
+ <a href="https://www.lri.fr/~speres">Sabine Peres</a> Université Paris Sud <br/>
+ <a href="http://iml.univ-mrs.fr/~remy/remy.html">Elisabeth Remy</a> Université de Marseille<br/>
+ <a href="http://www.irisa.fr/dyliss/anne.siegel">Anne Siegel</a> Inria Rennes <br/>
+ <a href="https://www.researchgate.net/profile/Moritz_Von_Stosch">Moritz von Stosch</a> Newcastle University<br/>
+ <a href="http://www.supbiotech.fr/professeurs-biotechnologie.aspx">Jean-Yves Trosset</a> Sup’Biotech, Paris Sud<br/>
+ <a href="http://www.cristal.univ-lille.fr/~versaric">Cristian Versari</a> Université de Lille <br/>
+
+ </p>
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
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diff --git a/manif/nantes_201509/nantes201509.html b/manif/nantes_201509/nantes201509.html
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--- /dev/null
+++ b/manif/nantes_201509/nantes201509.html
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+<!DOCTYPE HTML>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
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+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
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+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="../../img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="../../img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
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+ <img src="../../img/logoIM.jpg" alt="GDR Informatique
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+ </div>
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+ <!-- insert the page content here -->
+ <h1>1st Bioss Workshop: "Systemic Symbolic Biology"<br/><br/>
+ <i>September 18th, 2015</i>
+ </h1>
+
+ <h3>Objectives</h3>
+ <p align="justify">
+ Bioss is a new French working group on the symbolic modelling of
+ biologic systems, combining discrete mathematics and fundamental
+ computer science with molecular biology and medicine. This thematic
+ aims at studying information transmissions in biological systems,
+ by using formal methods from computer science (or new ones) in order
+ to model, analyse and understand the dynamics of complex living systems.<br>
+ This first workshop aims at bringing together the members of the
+ Bioss working group as well as anyone interested in the Bioss topics,
+ and to discuss the current activities of our research interests. It will
+ concentrate on the young generation, by giving invited PhD students and
+ postdocs the opportunity to present their recent or current work. This
+ will give a panorama, representative of the French activities on Symbolic
+ Systems Biology.
+ </p>
+
+ <h3>Scope</h3>
+ <p align="justify">
+ The program will consist of invited talks, mainly by PhD students and postdocs.<br>
+ Topics of Symbolic Systems Biology include, but are not limited to:<br>
+ <ul>
+ <li> modelling of biological systems, </li>
+ <li> discrete or hybrid formalisms, </li>
+ <li> semantics (included stochastic), </li>
+ <li> model verification, </li>
+ <li> model reduction, </li>
+ <li> prediction (under uncertainty), </li>
+ <li> model inference. </li>
+ </ul>
+ </p>
+
+ <h3>Registration</h3>
+ <p align="justify">
+ Registration is free but mandatory through the CMSB <a href="http://dr17.azur-colloque.cnrs.fr/pre-inscription.php?colloque=85&lang=en">registration page</a>, even if you don't attend CMSB.
+ </p>
+
+ <h3>Programme</h3>
+ <p align="left">
+ <b>14:30 - 14:55</b> <br>
+ Marc Bouffard -
+ <i> Extracting logic gates from a metabolic network, why and how ? </i><br>
+ <b>14:55 - 15:20</b> <br>
+ Pauline Traynard -
+ <i> Logical modeling and model-checking of the mammalian cell cycle </i><br>
+ <b>15:20 - 15:45</b> <br>
+ Julie Laniau -
+ <i> Metabolic network reconstruction with combinatorial
+ optimization : influence of cross-compartment metabolites </i><br>
+ <b>15:45 - 16:00</b> <br>
+ <i>Coffee break</i> <br>
+ <b>16:00 - 16:25</b> <br>
+ Alexandre Rocca -
+ <i> Application of Formal Methods to Biological Systems Modelling </i><br>
+ <b>16:25 - 16:50</b> <br>
+ Alexandre Temperville -
+ <i> Computation of sparse conservation laws and applications </i><br>
+ <b>16:50 - 17:15</b> <br>
+ Louis Fippo-Fitime -
+ <i> Integrating time-series data on large-scale discrete cell-based models </i><br>
+ <b>17:15 - 17:40</b> <br>
+ Pierre Boutillier -
+ <i> A new simulator for large Kappa models </i><br>
+ </p>
+
+
+ <h3>Abstracts</h3>
+ <p align="justify">
+ <b>Marc Bouffard (Univ. Paris Sud / LRI)</b> -
+ <i>Extracting logic gates from a metabolic network, why and how ?</i><br>
+ Synthetic biology can be used to design artificial
+ bio-devices that can perform successively the samples intake, its
+ analysis and provide an integrated response. This will be performed by a
+ logic function applied to the output of the sensors that respond to the
+ bio-markers of the targeted pathology. This logic function is computed
+ using logic circuits made of interconnected logic gates. For this
+ purpose, a library of basic logic gates that can be wired together so as
+ to function correctly in the same environment, will be automatically
+ extracted from the metabolic networks of living organisms.
+ <br><br>
+
+ <b>Pauline Traynard (Inria Paris-Rocquencourt)</b> -
+ <i>Logical modeling and model-checking of the mammalian cell cycle</i><br>
+ The molecular networks controlling cell cycle progression
+ have been predominantly modelled using differential equations, an
+ approach which demands to define complex regulatory terms with poorly
+ characterised kinetic parameters. In contrast, qualitative dynamical
+ models are easier to define, analyse and compose.<br>
+ We revisit a boolean model for the core network behind the mammalian
+ cell cycle (Fauré et al. Bioinformatics, 2006), taking into account
+ recent advances in the characterisation of the underlying molecular
+ networks to obtain a better qualitative consistency between simulations
+ and documented mutants features. In particular, we refine the model to
+ account for the role of the tumour suppressor protein Rb as a
+ multifunctional protein, involved in independent proliferative control
+ mechanisms. Using a multilevel rather than a boolean variable models the
+ fact that differently phosphorylated forms of Rb result in different
+ effects on other components of the network. We then introduce a crucial
+ regulatory link between Rb and p27, another important cell cycle
+ repressor.<br>
+ We evaluate the dynamical properties of the resulting model with
+ synchronous and asynchronous simulations using the software GINsim
+ (http://www.ginsim.org). We also have designed temporal logic queries,
+ enabling an efficient and automatic verification of key dynamical
+ properties such as conditions on the activation of components or the
+ order of changes of their levels, with the symbolic model checker
+ NuSMV. Moreover, adding transition probabilities allow a stochastic
+ simulation of the model with the software MaBoSS and provides new
+ insights into the dynamical behavior of the system.
+ <br><br>
+
+ <b>Julie Laniau (Inria Rennes)</b> -
+ <i>Metabolic network reconstruction with combinatorial
+ optimization : influence of cross-compartment metabolites</i><br>
+ <br>
+
+ <b>Alexandre Rocca (Univ. Joseph Fourier / Verimag)</b> -
+ <i>Application of Formal Methods to Biological Systems Modelling</i><br>
+ Modelling complex biological systems as differential
+ equations mostly results in non-linear, high-dimensional, and stiff
+ models. The validation of those models according to experimental results
+ is often done with simulation. Moreover, to cope with the uncertainty of
+ real biological systems, and to expand the experimental results to a
+ more general model, uncertain parameters are used.To validate a large
+ set of initial states and parameter values, a large number of simulation
+ runs is needed. However, the result, being non-exhaustive, is not
+ guaranteed. Formal verification techniques allow proving properties,
+ with set-based reachability computation techniques, by replacing
+ simulation runs with conservative sets of trajectories. Applying the
+ Bernstein expansion of polynomials, we developed a reachability tool on
+ polynomial systems with uncertain parameters. This tool takes advantage
+ of the particular forms of the polynomials modelling of biochemical
+ networks to speed up the Bernstein expansion.This allows a faster
+ verification of complex biological systems. The tool is directly applied
+ on a real system (Iron Homeostasis project). It is developed in
+ collaboration with a biological experimentation team (LBFA, Grenoble) to
+ address the needs of biologists.
+ <br><br>
+
+ <b>Alexandre Temperville (Univ. Lille 1 / CRIStAL)</b> -
+ <i>Computation of sparse conservation laws and applications</i><br>
+ Conservation laws are a key-tool to study systems of chemical
+ reactions in biology. After discussing about what good conservation laws
+ can be, we propose a greedy algorithm, computing a sparsest set of
+ conservation laws from a given set of conservation laws. Then, we
+ briefly present an improvement of this algorithm, some benchmarks on
+ Maple and Python codes of this algorihtm over a subset of the curated
+ models taken from the BioModels database. At last, we will finish to
+ talk about prospects of practical applications of this algorithm.
+ <br><br>
+
+ <b>Louis Fippo-Fitime (École Centrale de Nantes / IRCCYN)</b> -
+ <i>Integrating time-series data on large-scale discrete cell-based models</i><br>
+ In this work we propose an automatic way of generating and
+ verifying formal hybrid models of signaling and transcriptional events,
+ gathered in large-scale regulatory networks.This is done by integrating
+ temporal and stochastic aspects of the expression of some biological
+ com- ponents. The hybrid approach lies in the fact that measurements
+ take into account both times of lengthening phases and discrete switches
+ between them. The model proposed is based on a real case study of
+ keratinocytes differentiation, in which gene time-series data was
+ generated upon Calcium stimulation.<br>
+ To achieve this we rely on the Process Hitting (PH) formalism that was
+ designed to consider large-scale system analysis. We first propose an
+ automatic way of detecting and translating biological motifs from the
+ PID database to the PH formalism. Then, we propose a way of estimating
+ temporal and stochastic parameters from time-series expression data of
+ action on the PH. Simulations emphasize the interest of synchronizing
+ concurrent events.
+ <br><br>
+
+ <b>Pierre Boutillier (Univ. Paris Diderot / PPS)</b> -
+ <i>A new simulator for large Kappa models</i><br>
+ When trying to model biological systems, one can either rely
+ on so called "chemical reaction network" representations and use
+ techniques based on multisets rewriting or ODEs for simulations. An
+ alternative is to use representations that rely on reaction patterns
+ also called rule-based. While the first method is extremely efficient
+ when it applies, it requires somehow to compute the space of reachable
+ species and is subject to combinatorial explosion as the number of
+ simulated reactions grows rapidly. Kappa is a language based on graph
+ pattern rewriting, that attemps to cope with the combinatorics of
+ signalling pathways. A simulation step is based on Gillespie's algorithm
+ and requires to maintain all rule's left hand sides matches at each
+ state of the system. Keeping these matches up-to date is the bottleneck
+ of the simulator. In this talk I will describe how we attempt to
+ minimize the cost of this update operation.
+ <br>
+ </p>
+
+ <p style="text-indent:2em" align="justify"></p>
+ </div>
+
+ <!-- <div id="banner"></div> -->
+
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diff --git a/manif/nantes_201907/nantes_201907.html b/manif/nantes_201907/nantes_201907.html
new file mode 100644
index 0000000000000000000000000000000000000000..5ff735cc26124b6f90a0642c9ad72a28398dddeb
--- /dev/null
+++ b/manif/nantes_201907/nantes_201907.html
@@ -0,0 +1,204 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques
+ discrets, Systèmes formels,
+ Langages de modélisation,
+ Sémantique, Vérification de
+ modèles, Réduction, Prédiction
+ sous incertitude, Inférences
+ d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html; charset=UTF-8">
+
+ <link rel="stylesheet" type="text/css" href="../../style/style.css">
+</head>
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+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
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+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
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+ moléculaire"></a></li>
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+ mathématique"></a></li>
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+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+ <h1>Journées Bioss-Médecine-Personalisée</h1>
+
+ <h3>Informations générales</h3>
+ <p align="justify">
+ <b>Date</b> : 1er et 2 juillet (matin) 2019
+ </p>
+ <p align="justify">
+ <b>Lieu</b> : Salles A-B-C, Bâtiment 34, LS2N - Laboratoire des Sciences du Numérique de Nantes, Université de Nantes Faculté des Sciences et Techniques, 2 Chemin de la Houssinière, 44322 Nantes
+ </p>
+
+ <p align="justify">
+ <b>Organisatrice</b> : Carito GUZIOLOWSKI
+ </p>
+
+ <p align="justify">
+ <b>Presentation</b> :
+ The objective of the "BIOSS Personalized Medicine" meeting is to present and discuss informally numerical and mathematical modeling frameworks applied to understand medically important Human states. Talks are reassembled on discussing results that combine networks and (mathematical, probabilistic, logic, machine learning, among others) models. These models, integrate experimental or clinical observations and propose a computable representation of Human cellular, tissues, and clinical states related to unhealthy behaviors and cellular differentiation.
+ </p>
+
+ <h4>Lundi 1er Juillet</h4>
+ <p align="left">
+ <b>09h00 - 09h10</b> - <b>Accueil des participants</b><br>
+ <b>09h10 - 09h15</b> - <b>Ouverture</b><br>
+ <b>09h15 - 09h35</b> - Maxime Folschette (LS2N, Nantes) - <i>Search of Therapeutic Targets on the Hepatocellular Carcinoma with Database Extraction and Graph Coloring Methods.</i><br>
+ <b>09h40 - 10h00</b> - Lokmane Chebouba (LS2N, Nantes) - <i>Proteomics measurements combined with constraint programming for predicting treatment response in Acute Myeloid Leukemia cancer case.</i><br>
+ <b>10h00 - 10h45</b> - Benno Schwikowski (Institut Pasteur, Paris) -
+ <i>Interpretable machine learning to discover and map physiological activity using omics data.</i> <br>
+
+ <b>10h45 - 11h15</b> - <b>Pause café</b><br>
+ <b>11h15 - 12h00</b> - Diana Mateus (LS2N, Nantes) - <i>Prognosis Prediction of Myeloma Patients with Random Survival Forests.</i>
+ <br>
+ <b>12h00 - 12h45</b> - Herve Isambert (Institut Curie, Paris) - <i>Learning clinical networks from medical records based on information estimates in mixed-type data</i> <br>
+
+ <b>12h45 - 14h00 - Pause déjeuner</b><br><br>
+ <b>14h00 - 14h45</b> - Denis Thieffry (IBENS, Paris) - <i>Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4+ T cell activation.</i> <br>
+ <b>14h45 - 15h30</b> - Samuel Chaffron (LS2N, Nantes) - <i>Human gut microbiome co-activity networks in heath and disease.</i> <br>
+ <b>15h30 - 15h45</b> - <b>Pause café</b><br>
+ <b>15h45 - 16h30</b> - Laurence Calzone (Institut Curie, Paris) - <i>Une méthodologie de personalisation des modèles Booléens pour tester des inhibiteurs, simples ou doubles, avec des réponses qui varient selon les profils de patients.</i><br>
+ <b>16h30 - 17h15</b> - Celia Biane (IRISA, Rennes) - <i>Different approaches for the identification of perturbations in Boolean networks</i><br>
+ </p>
+
+ <h4>Mardi 2 Juillet</h4>
+ <p align="left">
+ <b>09h00 - 09h15</b> - <b>Accueil des participants</b><br>
+ <b>09h15 - 10h00</b> - Loic Paulevé (LaBRI, Bordeaux) - <i>Most Permissive Boolean Networks: Application to Inference of Models of Cellular Differentiation</i><br>
+ <b>10h00 - 10h45</b> - Dimitri Meistermann (CRTI, LS2N, Nantes) - <i>The limit of cell specification concept: a lesson from scRNA-Seq on early human development.</i> <br>
+
+ <b>10h45 - 11h15</b> - <b>Pause café</b><br>
+ <b>11h15 - 12h00</b> - Vera Pancaldi (CRCT, Toulouse) - <i>Quantification of tumour-infiltrating immune cells and beyond: modelling of cellular interactions in the tumour micro-environment.</i>
+ <br>
+ <b>12h00 - 12h15</b> - <b>Clôture et annonces</b>
+ </p>
+
+ </br>
+ <!-- <div id="banner"></div> -->
+ <h3>Abstracts</h3>
+ <p> <b>Diana Mateus</b> - <i>Prognosis Prediction of Myeloma Patients with Random Survival Forests.</i> <br/>
+ Multiple myeloma (MM) is a bone marrow cancer that accounts for 10\% of all hematological malignancies. FDG PET Quantitative imaging has great importance for its treatment protocol guidance. In this study, we aim to develop a computer-assisted method based on PET imaging features towards assisting personalized diagnosis and treatment decisions for MM patients. We consider texture-based (radiomics) features on top of conventional (e.g. SUVmax) and clinical biomarkers, resulting in a large input/feature vector. Our proposed model relies on a two-stage Random Survival Forest (RFS) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival(PFS), that is, the period of time until the first progression or relapse. We demonstrate the performance of the proposed approach in terms of C-index and final prognosis separation on a database of 66 patients who were part of the prospective multi-centric french IMAJEM study. Our results confirm the predictive value of radiomics for MM patients. Indeed, quantitative/heterogeneity image-based features reduce the error of the predicted progression.
+ </p>
+ <p> <b>Celia Biane</b> - <i>Different approaches for the identification of perturbations in Boolean networks.</i> </br/>
+ Boolean networks are discrete dynamical systems that are increasingly used in the field of systems biology to understand how complex cellular behaviors (phenotypes) emerge from the interaction of their molecular components. In this context, the interacting elements of the network represent diverse molecules whose local Boolean state is influenced by the state of other elements of the network, and the asymptotic states of the network represent the phenotype. During the last few years, different modelling and algorithmic approaches have been proposed for the identification of sets of local perturbations leading to a goal asymptotic behavior. During this presentation, I will propose different criteria of comparison of these approaches, show their application on a published model of bladder cancer and discuss their interpretation in the context of personalized/precision medicine.
+ </p>
+ <p>
+ <b>Benno Schwikowski</b> - <i>Interpretable machine learning to discover and map physiological activity using omics data.</i><br/>
+The activation or deactivation of most physiological processes in health and disease can be expected to be reflected in coordinated changes at the molecular level. The discovery and mapping of physiological processes from transcriptomic data can thus be attempted, for example, using models that are based on the quantification of single RNAs, or linear combinations thereof. The underlying biology reality is often likely to be more complex than this, but limited data availability and limited computational resources make it difficult to go beyond these simple models while preserving statistical power and interpretability of the results.<br/>
+In my talk, I will discuss two instances of new and carefully calibrated data analysis approaches that allowed us to discover and validate previously unknown associations between transcriptomic data and biomedically relevant physiology. Both models are highly interpretable and generic enough to be applied to a wide range omics analysis scenarios.<br/>
+References: Gwinner et al. (2017), https://doi.org/10.1093/bioinformatics/btw676, and Nikolayeva et al. (2018), https://doi.org/10.1093/infdis/jiy086
+ </p>
+ <p><b>Loic Paulevé</b> - <i>Most Permissive Boolean Networks: Application to Inference of Models of Cellular Differentiation</i><br/>
+ Boolean networks are a commonly used framework to model dynamics of large-scale interaction networks. They aim at enabling to reason on temporal behaviours of networks without requiring precise knowledge on kinetics and interaction thresholds.<br/>
+However, their usual interpretation can lead to wrong conclusions on their capability to reach certain behaviours. More precisely, refining a Boolean network model (with multivalued or ODEs for instance) can restrict some behaviours, but also create new ones, which are not predicted at the Boolean level.<br/>
+This is problematic when inferring networks at the Boolean level, as it leads to reject actually valid models, hence introducing bias in the analysis of candidate models of cellular processes.<br/>
+We introduce a new interpretation of Boolean networks which fixes this issue: with Most Permissive Boolean Networks, it is guaranteed that model refinements only restrict the capabilities of the model, thus allowing a correct abstract reasoning.<br/>
+Moreover, Most Permissive Boolean Networks are also much more tractable to analyse and do not suffer from the state space explosion.<br/>
+We illustrate their application to a scalable inference of models of cellular differentiation, which involve thorough constraints on the global dynamics of the network.
+ </p>
+ <p> <b>Dimitri Meistermann</b> - <i>The limit of cell specification concept: a lesson from scRNA-Seq on early human development.</i> (joint work with Sophie Loubersac, Arnaud Reignier, Valentin Francois-Campion, Thomas Fréour, Jérémie Bourdon and Laurent David)<br>
+Recent technological advances such as single-cell RNAseq have allowed an unprecedented access into processes orchestrating human preimplantation development [1, 2]. However, the sequence of events which occur during human preimplantation development are still unknown. In particular, timing of the very first human lineage specification remains elusive. During this event, the morula cells are can acquire two fates: the trophectoderm that will give rise the placenta and inner cell mass that will give rise the fetus. We present a human preimplantation development model based on transcriptomic pseudotime modelling of four scRNAseq dataset, biologically validated by spatial information and precise time-lapse staging. In contrast to mouse [3], we show that trophectoderm / inner cell mass lineage specification in human is only detectable at the transcriptomic level at the blastocyst stage, just prior to expansion. By studying this delay, we show that cellular specification is a time window that begins with the establishment of cellular junctions, which polarize the embryo. These are the first factors that discriminates the two cell fates. The cell specification ends with the divergence of transcriptome profiles. For identifying the precise timings of this divergence, we have coupled the pseudotime modelling from Monocle2 [4] with several other tools. First, we performed an estimation of RNA velocity with velocyto [5]. This tool can retrieve the genes that are going to be down or upregulated in each cell, by processing the intron data that are contained into scRNAseq reads. We used WGCNA [6] for describing the waves of genes that paces human preimplantation development. By combining these tools, we found novel markers, validated by immunofluorescences. Their expression profile enables a precise staging of human preimplantation embryos, such as IFI16 which highlights establishment of epiblast and NR2F2 which appears at the transition from specified to mature trophectoderm. Strikingly, mature trophectoderm cells arise from the polar side, just after specification, supporting a model of polar trophectoderm cells driving trophectoderm maturation. Altogether, our study unravels the first lineage specification event in the human embryo and provides a browsable resource, based on d3.js, for mapping spatio-temporal events underlying human lineage specification.<br/>
+References<br/>
+[1] L. Yan et al., « Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells », Nature Structural and Molecular Biology, vol. 20, no 9, p. 1131, sept. 2013.<br/>
+[2] S. Petropoulos et al., « Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos », Cell, vol. 165, no 4, p. 1012‐1026, mai 2016.<br/>
+[3] E. Posfai et al., « Position- and Hippo signaling-dependent plasticity during lineage segregation in the early mouse embryo », eLife, vol. 6.<br/>
+[4] C. Trapnell et al., « The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells », Nature Biotechnology, vol. 32, no 4, p. 381‐386, mars 2014.<br/>
+[5] G. La Manno et al., « RNA velocity of single cells », Nature, vol. 560, no 7719, p. 494‐498, août 2018.<br/>
+[6] P. Langfelder et S. Horvath, « WGCNA: an R package for weighted correlation network analysis », BMC
+Bioinformatics, vol. 9, p. 559, 2008.<br/>
+ </p>
+<p> <b>Maxime Folschette </b> - <i> Hepatocellular carcinoma (HCC) is the most widespread and lethal type of liver cancer today. Understanding the causes of its proliferation is thus a major challenge.</i><br/>
+In this work, we extract new biological regarding HCC proliferation based on a signaling network and partial observations of its components. Our network is extracted from Kegg, although Pathway Commons and other databases are also candidates. The observations come from a differential analysis of gene expression between invasive and non-invasive tumor tissues. Based on this initial data, we run a prediction algorithm called Iggy which extracts new knowledge when the observations are sufficient. The results illustrate the statistical precision of our computational predictions and exposes new knowledge concerning the activity of three protein-complexes (NFKB1::BCL3, NFKB2::RELB and JUND::NACA) which are validated through functional analyses and literature review on HCC.</p>
+<p> Otoniel Rodríguez-Jorge, Linda A. Kempis-Calanis, Wassim Abou-Jaoudé, Darely Y. Gutiérrez-Reyna, Céline Hernandez, Oscar Ramirez-Pliego, Morgane Thomas-Chollier, Salvatore Spicuglia, Maria A. Santana, <b>Denis Thieffry</b> - <i>Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4+ T cell activation</i><br/>
+CD4+ T cells recognize antigens through their T cell receptors. However, additional signals involving co-stimulatory receptors, for example CD28, are required for proper T cell activation. Alternative co-stimulatory receptors have been proposed, including members of the Toll-like receptor family, such as TLR5 and TLR2. However, the molecular mechanism underlying this co-stimulatory function has not yet been fully elucidated.<br/>
+Here, we report the generation of detailed molecular maps and logical models for the T cell receptor (TCR) and Toll-like receptor (TLR5) signalling pathways, along with a merged model accounting for cross-interactions. Furthermore, we validated the resulting model by analysing the responses of T cells to the activation of these pathways alone or in combination, in terms of CREB, AP-1 (c-Jun) and NF-kB (p65) activation. <br/>
+Our merged model accurately reproduces the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28, regarding AP-1, CREB and NF-кB activation, thereby, providing novel insights regarding cross-regulations of these pathways in CD4+ T cells.</p>
+<p> <b>Samuel Chaffron</b> - <i>Human gut microbiome co-activity networks in heath and disease</i><br/>
+Microbial communities inhabiting our intestinal tract impact and influence our nutrition, immunity and development. Today, High-Throughput Sequencing and functional genomics are revealing the under-explored diversity and complexity of these microbial ecosystems. Limited by the fact that most microbes can hardly be isolated and cultivated in lab-controlled environments, we are just starting to grasp the complexity and diversity of microbial interactions. Even when successful, laboratory experiments inherently lose valuable information about the richness and diversity of community functioning and interactions in situ. Today, large scale environmental surveys of microbial communities across Earth's ecosystems (e.g. Tara Oceans expeditions, integrative Human Microbiome Project) gathered large volumes of meta-omic and contextual data that are enabling the reconstruction of genomes of uncultivated microbial species or Metagenome-Assembled Genomes (MAGs). While classical co-occurrence analyses enable to predict interactions between newly identified microbes, these approaches are inherently limited since true biotic interactions can hardly be disentangle from abiotic (environmental) effects. Here, we developed a trait-based approach to enrich co-occurring information and uncover putative biotic interactions among human gut MAGs. Genomic and growth traits can directly be inferred from MAGs and meta-omics data. Here, co-growth signals across individuals are used to reveal positive and negative putative interactions between co-occurring microbes. In addition, the functional content of MAGs and the reconstruction of their metabolism will be used to predict and model potential microorganisms’ dependencies. Inferring and combining (meta-)genomic traits in a global approach can help to identify consortia of microbes and pave the way towards the functional understanding and the metabolic modeling of their interactions in health and disease.</p>
+<p><b>Laurence Calzone</b> - <i>Une méthodologie de personalisation des modèles Booléens pour tester des inhibiteurs, simples ou doubles, avec des réponses qui varient selon les profils de patients</i><br/>
+Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations or by inquiring pathway databases. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumours and their therapeutic responses. After introducing our approach for constructing logical models and simulating them stochastically, I will present the methodology for personalising logical models to data and show how these models can be used for testing the effect of drugs.
+</p>
+<p><b>Lokmane Chebouba</b> -<i>Proteomics measurements combined with constraint programming for predicting treatment response in Acute Myeloid Leukemia cancer case</i><br/>
+The use of data from high-throughput technologies to target drugs has been widespread in recent decades. Several approaches have been applied to biomedical data to detect disease-specific proteins and genes to better target drugs. We propose a new method for discriminating the response of patients with acute myeloid leukemia (AML) to treatments. The proposed approach uses proteomic data and the prior knowledge network to predict the results of cancer treatment by discovering the different Boolean networks specific to each type of treatment response.<br/>
+The results are encouraging and demonstrate the benefit of our approach to distinguish patient groups with different response to treatment. In particular each treatment response group is characterized by a predictive model in the form of a signaling Boolean network. This model describes regulatory mechanisms which are specific to each response group. This mechanistic and predictive model also allows us to classify new patients data into the two different patient response groups.
+</p>
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+<!DOCTYPE html>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques discrets,
+ Systèmes formels, Langages de modélisation,
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+ Réduction, Prédiction sous incertitude,
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+ <span class="logo_colour">Bioss</span>
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+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
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+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
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+ <!-- insert the page content here -->
+ <h1>Composition du groupe</h1>
+
+ <p align="justify">Nous présentons
+ ici les différents laboratoires et équipes de recherche
+ affiliés au groupe Bioss, par ordre alphabétique sur les
+ villes.</p>
+
+
+ <h4>Bordeaux</h4>
+ <ul>
+ <li>
+ Inria, Équipe Pleiade<br/>
+ Contact : David Sherman<br/>
+ Domaine : Systèmes dynamiques - Réseaux métaboliques - Écologie des systèmes - Génomique comparée - Optimisation combinatoire - Géométrie computationelle - Gestion de connaissances<br/>
+ Tutelles : Inria - INRAE - CNRS<br/>
+ URL : <a href="https://team.inria.fr/pleiade">https://team.inria.fr/pleiade</a>
+ </li>
+ <li> LaBRI, Équipe Image et Son<br>
+ Contact : Marie Beurton-Aimar
+ <br> Domaine : Analyse et visualisation de réseaux métaboliques, modélisation et simulation multi-agents, et apprentissage.
+<br>
+ url : http://www.labri.fr/index.php?n=ImageSon.ImageSon
+ </li>
+ <li> LaBRI, Département Méthodes Formelles<br/>
+ Contact : Loïc Paulevé <br/>
+ Domaine : Réseaux booléens, synthèse de modèles formels, programmation logique, reprogrammation cellulaire<br/>
+ Tutelles: Univ Bordeaux, CNRS (INS2I), Bordeaux INP<br/>
+ url : <a href="https://www.labri.fr/methodes-et-modeles-formels">https://www.labri.fr/methodes-et-modeles-formels</a>
+ </li>
+ </ul>
+ <!--
+ <h4>Chambéry</h4>
+ <ul>
+ <li> Fédération MSIF, Équipe Gémini<br> Contact : Laurent
+ Vuillon<br> Domaine : Systèmes dynamiques symboliques,
+ auto-assemblage<br> url : http://msif.cnrs.fr
+ </li>
+ </ul>-->
+ <h4>Évry</h4>
+ <ul>
+ <li>IBISC, Équipe COSMO<br> Contact : Franck Delaplace<br>
+ Domaine : Modèles concurrents, réseaux de Petri, réseaux
+ booléens, langages de modélisation et de programmation<br/>
+ Tutelles: Université Paris-Saclay<br/>
+ url : <a href="https://www.ibisc.univ-evry.fr/cosmo">https://www.ibisc.univ-evry.fr/cosmo</a>
+ </li>
+ </ul>
+
+ <h4>Grenoble</h4>
+ <ul>
+ <!--
+ <li> AGIM, Équipe GEM<br> Contact : Jacques Demongeot<br>
+ Domaine : Réseaux booléens et continus, déterministes ou
+ non, stochastiques ou non <br> url : http://www.agim.eu/equipes/gem
+ </li> -->
+ <li> INRIA, Équipe IBIS<br/>
+ Contact : Hidde de Jong<br/>
+ Domaine : Biologie systémique et synthétique bactérienne, intégration de données, systèmes dynamiques, théorie du contrôle, modèles stochastiques d'expression génique et de populations
+ analyse et simulation numériques <br/>
+ Tutelles: Inria, Université Grenoble Alpes, CNRS<br/>
+ url : <a href="http://team.inria.fr/ibis">http://team.inria.fr/ibis</a>
+ </li>
+ <li> TIMC-IMAG, Équipe BCM<br> Contact : Éric Fanchon<br>
+ Domaine : Modèles discrets, programmation logique, systèmes dynamiques continus et hybrides<br>
+ url : http://www-timc.imag.fr/rubrique44.html
+ </li>
+ </ul>
+
+ <h4>Jouy en Josas</h4>
+ <ul>
+ <li> INRAE, Unité MaIAGE<br> Contact : Laurent Tournier<br>
+ Domaine : Systèmes dynamiques, iBiologie des Systèmes<br>
+ url : <a href="https://maiage.jouy.inra.fr">https://maiage.jouy.inra.fr</a>
+ </li>
+ </ul>
+
+ <h4>Lille</h4>
+ <ul>
+ <!--
+ <li> Laboratoire Paul Painlevé, Équipe AGA<br> Contact :
+ Dmitry Grigoryev<br> Domaine : Géomémétrie algébrique,
+ théorie de la complexité<br> url :
+ http://math.univ-lille1.fr/d7/node/40
+ </li> -->
+ <li> CRIStAL, équipe BioComputing <br/>
+ Contact : Cédric Lhoussaine<br/>
+ Domaine : Analyse statique, Apprentissage, Réseaux métaboliques, Réseaux de réactions<br/>
+ Tutelles: CNRS (INS2I), Université de Lille, Centrale Lille, IMT Lille Douai<br/>
+ url : <a href="http://cristal.univ-lille.fr/BioComputing">http://cristal.univ-lille.fr/BioComputing</a>
+ </li>
+ <li> CRIStAL, Équipe Calcul Formel et Haute Performance <br> Contact : François Boulier<br/>
+ Domaine : Calcul symbolique<br/>
+ Tutelles: CNRS (INS2I), Université de Lille, Centrale Lille, IMT Lille Douai<br/>
+ url : <a href="http://cristal.univ-lille.fr/CFHP">http://cristal.univ-lille.fr/CFHP</a><br/>
+ </li>
+ </ul>
+
+ <h4>Lyon</h4>
+ <ul>
+ <li> LBMC, Équipe SBDM<br> Contact : Olivier Gandrillon<br>
+ Domaine : Prise de décision à l'échelle cellulaire<br>
+ url : <a href="http://www.ens-lyon.fr/LBMC/equipes/systems-biology-of-decision-making">http://www.ens-lyon.fr/LBMC/equipes/systems-biology-of-decision-making</a><br/>
+ Tutelles: CNRS, ENS Lyon, Université de Lyon 1, Inserm
+ </li>
+ <li> LBBE, Équipe ERABLE<br> Contact : Marie-France Sagot<br>
+ Domaine : Intégration de données, réseaux métaboliques<br>
+ url : <a href="https://team.inria.fr/erable/en/team-members">https://team.inria.fr/erable/en/team-members</a><br/>
+ Tutelles: Inria, CNRS, INSA Lyon
+ </li>
+ <li> LIRIS, Équipe BEAGLE<br> Contact : Guillaume Beslon<br>
+ Domaine : Évolution artificielle, dynamique inter-cellulaire<br>
+ url : <a href="https://team.inria.fr/beagle/">https://team.inria.fr/beagle/</a><br/>
+ Tutelles: Inria, INSA Lyon, Université Lyon 1
+ </li>
+ <li> LIP, Équipe PLUME<br> Contact : Russ Harmer<br>
+ Domaine : Modélisation à base de règles<br>
+ url : <a href="http://www.ens-lyon.fr/LIP/PLUME">http://www.ens-lyon.fr/LIP/PLUME</a><br/>
+ Tutelles: ENS Lyon, CNRS, Université Lyon 1
+ </li>
+ </ul>
+
+ <h4>Marseille</h4>
+ <ul>
+ <li> I2M, Équipe GDAC<br> Contact : Pierre Guillon<br>
+ Domaine : Systèmes dynamiques, pavages, automates
+ cellulaires<br> url :
+ https://www.i2m.univ-amu.fr/Equipe-Geometrie-Dynamique-Arithmetique
+ </li>
+ <li> I2M, Équipe Mabios<br> Contact : Élisabeth Remy<br>
+ Domaine : Modèles logiques, graphes biologiques, systèmes dynamiques discrets<br>
+ url : <a href="http://mabios.math.cnrs.fr">http://mabios.math.cnrs.fr</a><br/>
+ Tutelles: AMU, CNRS, École Centrale Marseille
+ </li>
+ <li> LIS (UMR7020), Équipe CANA<br/>
+ Contact : Sylvain Sené<br/>
+ Domaine : Calcul naturel, systèmes dynamiques, réseaux d'interactions, automates cellulaires<br/>
+ Tutelles: AMU, CNRS (INS2I)<br/>
+ url : <a href="https://cana.lis-lab.fr">https://cana.lis-lab.fr</a>
+ </li>
+ <li> MMG, Equipe NSBD<br/>
+ Contact : Anaïs Baudot<br/>
+ Domaine : Intégration de données, réseaux d’interactions biologiques multi-couches, analyse de données omiques<br/>
+ Tutelles: AMU, INSERM<br/>
+ url : <a href="https://www.marseille-medical-genetics.org/a-baudot">https://www.marseille-medical-genetics.org/a-baudot</a>
+ </li>
+ <li> MIO, Équipe EMBIO<br> Contact : David Nerini<br>
+ Domaine : Systèmes dynamiques, systèmes lents-rapides,
+ dynamique adaptative, analyse de données
+ fonctionnelles<br> url :
+ http://www.mio.univ-amu.fr/?-Equipe-5-Ecologie-Marine-et-
+ </li>
+ </ul>
+
+ <h4>Montpellier</h4>
+ <ul>
+ <li> DIMNP, Équipe Biophysique théorique et biologie des
+ systèmes<br> Contact : Ovidiu Radulescu<br> Domaine :
+ Réduction de modèles<br>
+ Tutelles: <br/>
+ url : <a href="http://www.dimnp.univ-montp2.fr/joomla/index.php?option=com_content&view=article&id=23&Itemid=131&lang=fr">https://lphi.umontpellier.fr/fr/les-equipes/biophysique-theorique-et-biologie-des-systemes</a>
+ </li>
+ </ul>
+
+ <h4>Nancy</h4>
+ <ul>
+ <li> LORIA, Équipe CARTE<br> Contact : Emmanuel Jeandel<br>
+ Domaine : Systèmes dynamiques, complexité, calculabilité<br>
+ url : http://carte.loria.fr/
+ </li>
+ </ul>
+
+ <h4>Nantes</h4>
+ <ul>
+ <li> IRCCyN, Équipe MÉFORBIO<br> Contact : Olivier Roux<br>
+ Domaine : Modèles concurrents, réseaux d'interactions,
+ formalisation des conniassances, programmation logique<br/>
+ Tutelles: CNRS, Université de Nantes, Centrale Nantes, IMT Atlantique<br/>
+ url : <a href="https://www.ls2n.fr/equipe/meforbio/">https://www.ls2n.fr/equipe/meforbio/</a>
+ </li>
+ <li> LINA, Équipe COMBI
+ <br/> Contact : Damien Eveillard<br/>
+ Domaine : modèles écologiques, modèles métaboliques, analyse et modélisation statique, combinatoire.<br/>
+ Tutelles: CNRS, Université de Nantes, Centrale Nantes, IMT Atlantique (partenaire INRIA)<br/>
+ url : <a href="https://www.ls2n.fr/equipe/combi/">https://www.ls2n.fr/equipe/combi</a>
+ </li>
+ </ul>
+
+ <h4>Nice</h4>
+ <ul>
+ <li> I3S, Équipe SPARKS<br> Contact : Jean-Paul Comet<br/>
+ Domaine : Réseaux discrets, méthodes formelles, vérification, systèmes dynamiques<br/>
+ Tutelles: Université Côte d'Azur, CNRS (INS2I)<br/>
+ url : <a href="http://www.i3s.unice.fr/sparks">http://www.i3s.unice.fr/sparks</a>
+ </li>
+ <li> I3S, Équipe MDSC<br> Contact : Enrico Formenti<br/>
+ Domaine : Réseaux booléens, méthodes formelles, systèmes
+ dynamiques, automates cellulaires<br/>
+ Tutelles: Université Côte d'Azur, CNRS (INS2I)<br/>
+ url : <a href="http://www.i3s.unice.fr/mdsc">http://www.i3s.unice.fr/mdsc</a>
+ </li>
+ <li> INRIA, Équipe BIOCORE<br> Contact : Madalena Chaves<br>
+ Domaine : Modèles booléens, contrôle, réduction<br>
+ Tutelles: Inria<br/>
+ url : <a href="https://team.inria.fr/biocore">https://team.inria.fr/biocore</a>
+ </li>
+ </ul>
+
+ <h4>Orsay</h4>
+ <ul>
+ <li> LRI, Équipe BIOINFO<br> Contact : Christine
+ Froidevaux<br> Domaine : Modèles concurrents,
+ représentation des connaissances, analyse et simulation
+ numériques de réseaux<br>
+ Tutelles:<br/>
+ url : <a href="https://www.lri.fr/equipe.php?eq=4">https://www.lri.fr/equipe.php?eq=4</a>
+ </li>
+ </ul>
+
+ <h4>Paris</h4>
+ <ul>
+ <li> DIENS, Équipe ANTIQUE<br> Contact : Jérôme Féret<br>
+ Domaine : Interprétation abstraite, analyse statique,
+ modélisation à base de règles<br> url :
+ http://www.di.ens.fr/AntiqueTeam.html.fr
+ </li>
+ <li> IBENS, Équipe CSB<br> Contact : Denis Thieffry<br>
+ Domaine : Modèles Booléens et multivalués, analyses de données omiques<br/>
+ Tutelles: ENS, CNRS (INSB), INSERM<br/>
+ url : <a href="https://www.ibens.ens.fr/csb">https://www.ibens.ens.fr/csb</a>
+ </li>
+ <li> INRIA, Équipe TAPDANCE<br> Contact: Damien Woods<br>
+ Domaine : Calcul naturel, auto-assemblage, automates
+ cellulaires<br> url : https://www.inria.fr/equipes/tapdance
+ <li>
+ <li> Institut Curie, Équipe BCSBC<br> Contact : Laurence
+ Calzone<br> Domaine : Modèles booléens, modèles
+ stochastique, simulations numériques<br> url :
+ http://ibis.inrialpes.fr
+ </li>
+ <li> INSERM U1138, Équipe KROEMER<br> Contact : Gautier Stoll<br>
+ Domaine : simulations logiques stochastiques<br> url :
+ http://www.crc.jussieu.fr/guido_kroemer2.html
+ </li>
+ <li> IRCAM, Équipe MRT<br> Contact : Jean-Louis Giavitto<br>
+ Domaine : Programmation spatiale, calcul amorphe,
+ simulations numériques, biologie synthétique <br> url :
+ http://repmus.ircam.fr/giavitto
+ </li>
+ <li> IRIF, Équipe ADG<br> Contact : Nicolas Schabanel<br>
+ Domaine : Systèmes dynamiques, théorie des graphes,
+ automates cellulaires, complexité <br> url :
+ https://www.irif.univ-paris-diderot.fr/equipes/adg/index
+ </li>
+ <li> IRIF, Équipe PPS<br> Contact : Jean Krivine<br> Domaine
+ : Modélisation à base de règles, modèles discrets <br> url
+ : http://www.irif.univ-paris-diderot.fr/~jkrivine
+ </li>
+ <li> LACL, Équipe LCP<br> Contact : Olivier Michel<br>
+ Domaine : Programmation spatiale, calcul amorphe,
+ simulations numériques <br> url : http://www.lacl.fr/fr/lcp
+ </li>
+ <li> MAS, Équipe LOGIMAS<br> Contact : Pascale Le Gall<br>
+ Domaine : Méthodes formelles, analyse, test,
+ vérification<br> url :
+ http://www.mas.ecp.fr/cms/lang/fr/home/recherche_mas/equipes/logimas
+ </li>
+ <li>
+ Inria / Institut Pasteur, Equipe InBio<br/>
+ Contact : Gregory Batt<br/>
+ Domaine : Biologie computationnelle, Biologie des systèmes, Biologie de synthèse, Modèles déterministes et stochastiques, Hétérogénéité cellulaire et dynamique de populations<br>
+ url : <a href="https://research.pasteur.fr/en/team/inbio/">https://research.pasteur.fr/en/team/inbio/</a><br>
+ Tutelles: Inria
+ </li>
+ </li>
+ </ul>
+
+ <h4>Rennes</h4>
+ <ul>
+ <li> IRISA, Équipe Dyliss<br> Contact : Anne Siegel<br>
+ Domaine : Réseaux métaboliques, Microbiome, Intégration multi-échelle, modèles booléens, Web sémantique<br/>
+ url : <a href="https://www-dyliss.irisa.fr">https://www-dyliss.irisa.fr</a><br/>
+ Tutelles: Université de Rennes 1, Inria, CNRS (INS2I)
+ </li>
+ </ul>
+
+ <h4>Saclay</h4>
+ <ul>
+ <li> INRIA, Équipe Lifeware<br> Contact : François Fages<br>
+ Domaine : Vérification et synthèse de paramètres,
+ programmation par contraintes, réduction, liens
+ structure/dynamique<br> url : http://lifeware.inria.fr
+ </li>
+ <li> LIX, Équipe AMIB<br> Contact : Mireille Régnier<br>
+ Domaine : Modélisation métabolique<br>
+ url : https://team.inria.fr/amib/fr
+ </li>
+ </ul>
+
+ <h4>Tours</h4>
+ <ul>
+ <li> PRC INRAE, équipe BIOS<br/>
+ Contact: Romain Yvinec<br/>
+ Domaine: réseaux signalisation cellulaire des récepteurs couplés aux protéines G, modélisation dynamique déterministe et stochastique (ODE,CTMC), inférence de paramètres<br/>
+ Tutelles: INRAAE, CNRS, Université de Tours<br/>
+ url: <a href="http://bios.tours.inra.fr/bios_group">http://bios.tours.inra.fr/bios_group</a><br/>
+ </li>
+ </ul>
+ </div>
+ <!-- <div id="banner"></div> --> </div>
+
+ <div id="footer">
+ <p><a href="index.html">Accueil</a>
+ | <a href="membres.html">Membres</a>
+ | <a href="manif.html">Manifestations</a>
+ | <a href="projets.html">Projets</a>
+ | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+
+</body></html>
diff --git a/projets.html b/projets.html
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+<!DOCTYPE html>
+<html>
+
+<head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie systémique symbolique">
+
+ <meta name="keywords" content="Modélisation symbolique de systèmes
+ biologiques, Systèmes dynamiques discrets,
+ Systèmes formels, Langages de modélisation,
+ Sémantique, Vérification de modèles,
+ Réduction, Prédiction sous incertitude,
+ Inférences d'interactions et de règles,
+ Systèmes hybrides">
+
+ <meta http-equiv="content-type" content="text/html;
+ charset=utf-8">
+
+ <link rel="stylesheet" type="text/css" href="style/style.css">
+</head>
+
+<body>
+ <div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text">
+ <!-- class="logo_colour", allows you to change the colour of
+ the text -->
+ <h1><a href="index.html">
+ <span class="logo_colour">Bioss</span>
+ </a></h1>
+ <h2>Groupe de travail sur la biologie systémique symbolique</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected page - to
+ highlight which page you're on -->
+ <li><a href="index.html">Accueil</a></li>
+ <li><a href="membres.html">Membres</a></li>
+ <li><a href="manif.html">Manifestations</a></li>
+ <li class="selected"><a href="projets.html">Projets</a></li>
+ <li><a href="contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="site_content">
+ <!-- <div id="banner"></div> -->
+
+ <div id="sidebar_container">
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item"></div>
+ <div class="sidebar_base"></div>
+ </div>
+
+ <div class="sidebar">
+ <div class="sidebar_top"></div>
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">
+ <img src="img/logoCNRS.jpg" alt="CNRS"></a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">
+ <img src="img/logoBIM.jpg" alt="GDR Bioinformatique
+ moléculaire"></a></li>
+ <li><a href="https://www.gdr-im.fr/">
+ <img src="img/logoIM.jpg" alt="GDR Informatique
+ mathématique"></a></li>
+ </ul>
+ </div>
+ <div class="sidebar_base"></div>
+ </div>
+ </div>
+
+ <div id="content">
+ <!-- insert the page content here -->
+
+ <h1>Contrats et projets en lien avec le groupe</h1>
+
+ <p align="justify">Sont présentés
+ ici par ordre antichronologique et de manière certainement non
+ exhaustive les projets d'envergure nationale et internationale des
+ différents acteurs du groupe Bioss.</p>
+
+ <h2>Projets nationaux</h2>
+
+ <ul>
+ <li>
+ ANR-FNR AlgoReCell 2016 - 36 mois (programme générique)
+ <br>Titre: Computational Models and Algorithms for the Prediction of Cell
+ Reprogramming Determinants with High Efficiency and High Fidelity
+ <br>Partenaires : LRI (Univ. Paris-Sud / CNRS); LSV/Inria Saclay; Institut Curie;
+ FSTC Life (Univ. Luxembourg); FSTC CSC (Univ. Luxembourg); Luxembourg Centre
+ for Systems Biomedicine (Univ. Luxembourg)<br>
+ <a href="http://algorecell.lri.fr">http://algorecell.lri.fr</a><br>
+ </li>
+ <li>
+ ANR CenTuri 2016 - 120 mois (programme IA Instituts
+ Convergences)<br> Titre : Centre Turing des systèmes
+ vivants<br> Partenaires: IBDM, CIML, INMED, I2M, CPT,
+ CINAM, Fresnel, IRPHé, IUSTI, LIF (Univ. Aix-Marseille /
+ CNRS), LAI (Univ. Aix-Marseille / APHM / INSERM),
+ Bio-AFM, TAGC (Univ. Aix-Marseille / INSERM), M2P2 (École
+ centrale Marseille / Univ. Aix-Marseille / CNRS) et CIPHE
+ (Univ. Aix-Marseille / INSERM / CNRS)<br>
+ </li>
+ <li>
+ ANR MEMIP 2016 - 48 mois (programme générique)<br> Titre :
+ Modèles à effets mixtes de processus intracellulaires :
+ méthodes, outils et applications<br> Partenaires :
+ Lifeware (Inria Saclay), MSC (Univ. Paris Diderot / CNRS),
+ IBIS (Inria Grenoble / Univ. Grenoble) et XPOP (Inria
+ Saclay / École Polytechnique)<br>
+ </li>
+ <li>
+ ANR ICycle 2016 -- ?? mois (programme générique)<br> Titre
+ : Interconnexion et contrôle de deux oscillateurs
+ biologiques dans des cellules mammaliennes<br> Partenaires
+ : Biocore (INRIA Sophia), iBV (Univ. Nice - Sophia / CNRS)
+ et Maiage-Biosys (INRA Jouy-en-Josas)<br>
+ </li>
+ <li>
+ ANR HYCLOCK 2015 - 48 mois (programme "Une nouvelle
+ représentation du vivant")<br> Titre : Modélisation hybride
+ formelle du temps pour la biologie des horloges
+ circadiennes et la chronopharmacologie<br> Partenaires :
+ iBV, I3S (Nice), INSERM UMR-S776 (Orsay), IRCCyN (Nantes),
+ Lifeware (INRIA Rocquencourt)<br>
+ </li>
+ <li>
+ ANR SAMOSA 2014 - 48 mois (programme BIOADAPT)<br>
+ Titre : Synechococcus as a model genus for studying adaptation of
+ marine phytoplankton to environmental changes<br>
+ Partenaires : ABIMS (Station Roscoff), LINA (Univ. Nantes), AD2M
+ (Univ. européenne Bretagne)<br>
+ </li>
+ <li>
+ ANR STOCH-MC 2014 - 48 mois (programme blanc SIMI 2)<br>
+ Titre : <br>
+ Partenaires : IRISA (INRIA Rennes), Lifeware (INRIA Rocquencourt),
+ LIAFA (Univ. Paris 7), LaBRI (Univ. Bordeaux)<br>
+ </li>
+ <li>
+ ANR CADMIDIA 2013 - 48 mois (programme CESA)<br>
+ Titre : Cadmium et diabète<br>
+ Partenaires : TIMC-IMAG (Univ. Grenoble), VERIMAG (Univ.
+ Grenoble), CBM (CEA Grenoble), LBFA (INSERM Grenoble)<br>
+ </li>
+ <li>
+ ANR Iceberg 2012 - 60 mois (programme IA Bio-informatique)<br>
+ Titre : Des modèles de population aux populations de modèles:
+ observation, modélisation et contrôle de l’expression génique au
+ niveau de la cellule unique<br>
+ Partenaires : Lifeware (INRIA Rocquencourt), LIFL (Univ. Lille),
+ PPS & IJM & MSC (Univ. Paris 7), CGMC (Univ. Lyon)
+ </li>
+ <li>
+ ANR RESET 2012 - 48 mois (programme IA Bio-informatique)<br>
+ Titre : Eteindre et rallumer la machinerie d'expression génique
+ chez les bactéries: de modèles mathématiques aux applications
+ biotechnologiques<br>
+ Partenaires : IBIS (INRIA Grenoble), LAPM (Univ. Grenoble),
+ LIPhy (Univ. Grenoble), BGE (CEA Grenoble), Génétique végétale
+ (INRA), BIOCORE (INRIA Sophia), Metabolic Explorer<br>
+ </li>
+ <li>
+ ANR Facteur 4 2012 - 36 mois (programme Bio-ME)<br>
+ Titre : Amélioration non OGM des performances de microalgues<br>
+ Partenaires : BIOCORE (INRIA Sophia), OOV (Univ. Paris 6), PBA
+ (IFREMER)<br>
+ </li>
+ <li>
+ ANR FatInteger 2012 - 36 mois (programme blanc SVSE 7)<br>
+ Titre : Recherche de régulateurs clefs de la plasticité lipidique
+ chez deux espèces monogastriques majeures (porc et poule) en
+ combinant des données haut débit et des approches statistique,
+ bioinformatique et phylogénique<br>
+ Partenaires : IRISA (CNRS Bretagne), GARen & SENAH & URA (INRA),
+ LMA-IRMAR (AGROCAMPUS Ouest)<br>
+ </li>
+ <li>
+ ANR MiRNAdapt 2012 - 36 mois (programme blanc SVSE 6)<br>
+ Titre : MicroARN et plasticité phénotypique<br>
+ Partenaires : IRISA (CNRS Bretagne), IGH (CNRS Languedoc),
+ BiO3P (INRA)<br>
+ </li>
+ <li>
+ ANR IDEALG 2011 - 96 mois (programme IA Santé-Biotechnologies)<br>
+ Titre : Étude génomique et post-génomique des algues pour le
+ développement de nouveaux outils et méthodes permettant
+ d’identifier et sélectionner des populations « ressource » locales
+ ayant un intérêt industriel<br>
+ Partenaires : LBI2M (Univ. Paris 6), IRISA (CNRS Bretagne), UFIP
+ (Univ. Nantes), CEVA, COS (ÉNSC Rennes), ALEOR, LBCM (Univ.
+ Bretagne sud), C-WEED, IFREMER Brest, BEZHIN ROSKO, FRANCE
+ HALIOTIS, AGROCAMPUS Ouest, AMURE (Univ Bretagne occidentale), LBE
+ (INRA), DuPont<br>
+ </li>
+ <li>
+ ANR STOCHAGENE 2011 - 48 mois (programme blanc SVSE 6)<br>
+ Titre : Rôle de la dynamique chromatinienne dans la stochasticité
+ de l’expression génique dans des cellules d’eucaryotes
+ supérieurs<br>
+ Partenaires : CGPMC (Univ. Lyon), LIRIS (INSA-Lyon), U951 (INSERM
+ Paris XII)<br>
+ </li>
+ <li>
+ ANR BioTempo 2011 - 42 mois (programme Blanc SIMI 2)<br>
+ Titre : Langages, concepts de temps et modèles hybrides pour
+ l'analyse de modèles incomplets en biologie moléculaire<br>
+ Partenaires : IRISA (CNRS Bretagne), I3S (CNRS Côte d'azur), LINA
+ (Univ. Nantes), IRCCyN (É.C. Nantes), Lifeware (INRIA
+ Rocquencourt), DIMNP (Univ. Montpellier), LBI2M (Univ. Paris 6),
+ SeRAIC (Univ. Rennes), Mathomics (Univ. Santiago), Institut für
+ Informatik (Univ. Postdam), Institut du thorax (Univ. Nantes)<br>
+ </li>
+ <li>
+ ANR SYNBIOTIC 2010 - 48 mois (programme blanc SIMI 3)<br>
+ Titre : Systèmes biologiques de synthèse : de la conception à la
+ compilation<br>
+ Partenaires : IBISC (Univ. Évry), LACL (Univ. Créteil), CREA
+ ISC-PIF (CNRS IDF Ouest et Nord)<br>
+ </li>
+ <li>
+ ANR GeMCo 2010 - 36 mois (programme blanc SIMI 2)<br>
+ Titre : Réduction, validation expérimentale et contrôle de
+ modèles de la machinerie d'expression génique chez E. Coli<br>
+ Partenaires : IBIS (INRIA Grenoble), BIOCORE (INRIA Sophia), LAPM
+ (Univ. Grenoble)<br>
+ </li>
+ </ul>
+
+ <h2>Projets internationaux</h2>
+
+ <ul>
+ <li>
+ ERC Advanced grant RULE 2013 - 60 mois (programme
+ FP7-IDEAS-ERC)<br>
+ Titre : Rule-based modelling<br>
+ Partenaires : Univ. Édimbourg</br>
+ </li>
+ <li>
+ EU EvoEvo 2013 - 36 mois (programme FP7-ICT)<br>
+ Titre : Evolution of evolution<br>
+ Partenaires : BEAGLE (INRIA Grenoble), LAPM (Univ. Grenoble),
+ Univ. Utrecht (Pays-Bas), Univ. York (U.K), CSIC (Spain)<br>
+ </li>
+ <li>
+ ERC Advanced grant SISYPHE 2010 - 60 mois (programme
+ FP7-IDEAS-ERC)<br>
+ Titre : Species identity and symbiosis formally and experimentally
+ explored<br>
+ Partenaires : BAMBOO (INRIA Grenoble)<br>
+ </li>
+ </ul>
+
+ <h2>Projets régionaux</h2>
+
+ <ul>
+ <li>
+ PACA FRI 2015 - 24 mois (programme APEX)<br>
+ Titre : Fondements des réseaux d'interactions<br>
+ Partenaires : LIF, I2M (Univ. Aix-Marseille), I3S (Univ.
+ Nice-Sophia)<br>
+ </li>
+ </ul>
+ </div>
+ <!-- <div id="banner"></div> --> </div>
+
+ <div id="footer">
+ <p><a href="index.html">Accueil</a>
+ | <a href="membres.html">Membres</a>
+ | <a href="manif.html">Manifestations</a>
+ | <a href="projets.html">Projets</a>
+ | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
+ Templates</a></p>
+ </div>
+ </div>
+
+</body></html>
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--- /dev/null
+++ b/svg/projets_svg.html
@@ -0,0 +1,236 @@
+<!DOCTYPE html>
+<html><head>
+ <title>Bioss</title>
+
+ <meta name="description" content="Page du groupe de travail sur la
+ biologie syst�mique symbolique"> <meta name="keywords" content="Mod�lisation symbolique de syst�mes biologiques, Syst�mes
+ dynamiques discrets, Syst�mes formels, Langages de mod�lisation,
+ S�mantique, V�rification de mod�les, R�duction, Pr�diction sous
+ incertitude, Inf�rences d'interactions et de r�gles, Syst�mes
+ hybrides"> <meta http-equiv="content-type" content="text/html; charset=windows-1252"> <link rel="stylesheet" type="text/css" href="style/style.css">
+
+ </head>
+
+<body>
+
+<div id="main">
+ <div id="header">
+ <div id="logo">
+ <div id="logo_text"> <!-- class="logo_colour", allows you to change the colour of the text -->
+ <h1><a href="index.html"><span class="logo_colour">Bioss</span></a></h1>
+ <h2>Groupe de travail sur la mod�lisation symbolique des
+ syst�mes biologiques</h2>
+ </div>
+ </div>
+ <div id="menubar">
+ <ul id="menu">
+ <!-- put class="selected" in the li tag for the selected page - to highlight which page you're on -->
+ <li><a href="index.html">Accueil</a></li>
+ <li><a href="membres.html">Membres</a></li>
+ <li class="selected"><a href="projets.html">Projets</a></li>
+ <li><a href="manif.html">Manifestations</a></li>
+ <li><a href="contact.html">Contacts</a></li>
+ </ul>
+ </div>
+ </div>
+
+ <div id="site_content">
+ <!-- <div id="banner"></div> -->
+ <div id="sidebar_container">
+ <div class="sidebar">
+
+ <div class="sidebar_item">
+ <!-- insert your sidebar items here -->
+ <h3>News</h3>
+ <h4>Site web lanc�</h4>
+ <h5>1er novembre 2014</h5>
+ <p>Bioss est enfin pr�sent sur la toile.<br>
+ <a href="#">+ d'infos</a></p>
+ </div>
+
+ </div>
+ <div class="sidebar">
+
+ <div class="sidebar_item">
+ <h3>Liens utiles</h3>
+ <ul>
+ <li><a href="http://www.cnrs.fr/">CNRS</a></li>
+ <li><a href="http://www.gdr-bim.cnrs.fr/">GDR BIM -
+ Bio-informatique mol�culaire</a></li>
+ <li><a href="https://www.gdr-im.fr/">GDR IM -
+ Informatique math�matique</a></li>
+ </ul>
+ </div>
+
+ </div>
+ </div>
+ <div id="content">
+ <!-- insert the page content here -->
+
+ <p style="text-indent: 2em;" align="justify">Sont pr�sent�s
+ ici par ordre antichronologique et de mani�re certainement non
+ exhaustive les projets d'envergure internationale et nationale des
+ diff�rents acteurs du groupe Bioss.</p>
+
+ <h1>Projets internationaux</h1>
+
+ <ol>
+ <li>
+ ERC Advanced grant RULE 2013 - 60 mois (programme
+ FP7-IDEAS-ERC)<br>
+ Titre : Rule-based modelling<br>
+ Partenaires : Univ. �dimbourg<br>
+ Budget : 2084316 euros
+ </li>
+ <li>
+ EU EvoEvo 2013 - 36 mois (programme FP7-ICT)<br>
+ Titre : Evolution of evolution<br>
+ Partenaires : BEAGLE (INRIA Grenoble), LAPM (Univ. Grenoble),
+ Univ. Utrecht (Pays-Bas), Univ. York (U.K), CSIC (Spain)<br>
+ Budget : 2629000 euros
+ </li>
+ <li>
+ ERC Advanced grant SISYPHE 2010 - 60 mois (programme
+ FP7-IDEAS-ERC)<br>
+ Titre : Species identity and symbiosis formally and experimentally
+ explored<br>
+ Partenaires : BAMBOO (INRIA Grenoble)<br>
+ Budget : 2333272 euros
+ </li>
+ </ol>
+
+ <h1>Projets nationaux</h1>
+
+ <ol>
+ <li>
+ ANR SAMOSA 2014 - 48 mois (programme BIOADAPT 2013)<br>
+ Titre : Synechococcus as a model genus for studying adaptation of
+ marine phytoplankton to environmental changes<br>
+ Partenaires : ABIMS (Station Roscoff), LINA (Univ. Nantes), AD2M
+ (Univ. europ�enne Bretagne)<br>
+ Budget : 458998 euros
+ </li>
+ <li>
+ ANR STOCH-MC 2014 - 48 mois (programme blanc SIMI 2)<br>
+ Titre : <br>
+ Partenaires : IRISA (INRIA Rennes), Lifeware (INRIA Rennes), LIAFA
+ (Univ. Paris 7), LaBRI (Univ. Bordeaux)<br>
+ Budget : 361900 euros
+ </li>
+ <li>
+ ANR CADMIDIA 2013 - 48 mois (programme CESA)<br>
+ Titre : Cadmium et diab�te<br>
+ Partenaires : TIMC-IMAG (Univ. Grenoble), VERIMAG (Univ.
+ Grenoble), CBM (CEA Grenoble), LBFA (INSERM Grenoble)<br>
+ Budget : 466960 euros
+ </li>
+ <li>
+ ANR Iceberg 2012 - 60 mois (programme IA Bio-informatique)<br>
+ Titre : Des mod�les de population aux populations de mod�les:
+ observation, mod�lisation et contr�le de l�expression g�nique au
+ niveau de la cellule unique<br>
+ Partenaires : Lifeware (INRIA Rocquencourt), LIFL (Univ. Lille),
+ PPS & IJM & MSC (Univ. Paris 7), CGMC (Univ. Lyon)
+ Budget : 1240000 euros
+ </li>
+ <li>
+ ANR RESET 2012 - 48 mois (programme IA Bio-informatique)<br>
+ Titre : Eteindre et rallumer la machinerie d'expression g�nique
+ chez les bact�ries: de mod�les math�matiques aux applications
+ biotechnologiques<br>
+ Partenaires : IBIS (INRIA Grenoble), LAPM (Univ. Grenoble),
+ LIPhy (Univ. Grenoble), BGE (CEA Grenoble), G�n�tique v�g�tale
+ (INRA), BIOCORE (INRIA Sophia), Metabolic Explorer<br>
+ Budget : 1500000 euros
+ </li>
+ <li>
+ ANR Facteur 4 2012 - 36 mois (programme Bio-ME)<br>
+ Titre : Am�lioration non OGM des performances de microalgues<br>
+ Partenaires : BIOCORE (INRIA Sophia), OOV (Univ. Paris 6), PBA
+ (IFREMER)<br>
+ Budget : 687940 euros
+ </li>
+ <li>
+ ANR FatInteger 2012 - 36 mois (programme blanc SVSE 7)<br>
+ Titre : Recherche de r�gulateurs clefs de la plasticit� lipidique
+ chez deux esp�ces monogastriques majeures (porc et poule) en
+ combinant des donn�es haut d�bit et des approches statistique,
+ bioinformatique et phylog�nique<br>
+ Partenaires : IRISA (CNRS Bretagne), GARen & SENAH & URA (INRA),
+ LMA-IRMAR (AGROCAMPUS Ouest)<br>
+ Budget : 384957 euros
+ </li>
+ <li>
+ ANR MiRNAdapt 2012 - 36 mois (programme blanc SVSE 6)<br>
+ Titre : MicroARN et plasticit� ph�notypique<br>
+ Partenaires : IRISA (CNRS Bretagne), IGH (CNRS Languedoc),
+ BiO3P (INRA)<br>
+ Budget : 226000 euros
+ </li>
+ <li>
+ ANR IDEALG 2011 - 96 mois (programme IA Sant�-Biotechnologies)<br>
+ Titre : �tude g�nomique et post-g�nomique des algues pour le
+ d�veloppement de nouveaux outils et m�thodes permettant
+ d�identifier et s�lectionner des populations � ressource � locales
+ ayant un int�r�t industriel<br>
+ Partenaires : LBI2M (Univ. Paris 6), IRISA (CNRS Bretagne), UFIP
+ (Univ. Nantes), CEVA, COS (�NSC Rennes), ALEOR, LBCM (Univ.
+ Bretagne sud), C-WEED, IFREMER Brest, BEZHIN ROSKO, FRANCE
+ HALIOTIS, AGROCAMPUS Ouest, AMURE (Univ Bretagne occidentale), LBE
+ (INRA), DuPont<br>
+ Budget : 10000000 euros
+ </li>
+ <li>
+ ANR STOCHAGENE 2011 - 48 mois (programme blanc SVSE 6)<br>
+ Titre : R�le de la dynamique chromatinienne dans la stochasticit�
+ de l�expression g�nique dans des cellules d�eucaryotes
+ sup�rieurs<br>
+ Partenaires : CGPMC (Univ. Lyon), LIRIS (INSA-Lyon), U951 (INSERM
+ Paris XII)<br>
+ Budget : 466000 euros
+ </li>
+ <li>
+ ANR BioTempo 2011 - 42 mois (programme Blanc SIMI 2)<br>
+ Titre : Langages, concepts de temps et mod�les hybrides pour
+ l'analyse de mod�les incomplets en biologie mol�culaire<br>
+ Partenaires : IRISA (CNRS Bretagne), I3S (CNRS C�te d'azur), LINA
+ (Univ. Nantes), IRCCyN (�.C. Nantes), Lifeware (INRIA
+ Rocquencourt), DIMNP (Univ. Montpellier), LBI2M (Univ. Paris 6),
+ SeRAIC (Univ. Rennes), Mathomics (Univ. Santiago), Institut f�r
+ Informatik (Univ. Postdam), Institut du thorax (Univ. Nantes)<br>
+ Budget : 310000 euros
+ </li>
+ <li>
+ ANR SYNBIOTIC 2010 - 48 mois (programme blanc SIMI 3)<br>
+ Titre : Syst�mes biologiques de synth�se : de la conception � la
+ compilation<br>
+ Partenaires : IBISC (Univ. �vry), LACL (Univ. Cr�teil), CREA
+ ISC-PIF (CNRS IDF Ouest et Nord)<br>
+ Budget : 539000 euros<br>
+ </li>
+ <li>
+ ANR GeMCo 2010 - 36 mois (programme blanc SIMI 2)<br>
+ Titre : R�duction, validation exp�rimentale et contr�le de
+ mod�les de la machinerie d'expression g�nique chez E. Coli<br>
+ Partenaires : IBIS (INRIA Grenoble), BIOCORE (INRIA Sophia), LAPM
+ (Univ. Grenoble)<br>
+ Budget : 444730 euros
+ </li>
+ </ol>
+ </div>
+ <!-- <div id="banner"></div> --> </div>
+
+ <div id="footer">
+ <p><a href="index.html">Accueil</a> | <a href="membres.html">Membres</a>
+ | <a href="projets.html">Projets</a> | <a href="manif.html">Manifestations</a>
+ | <a href="contact.html">Contact</a></p>
+ <p><a href="http://validator.w3.org/check?uri=referer">HTML5</a>
+ | <a href="http://jigsaw.w3.org/css-validator/check/referer">CSS</a>
+ | <a href="http://www.html5webtemplates.co.uk">HTML5 Web
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