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 symbolique des systèmes biologiques, thématique centrale de la biologie des systèmes à la frontière des mathématiques discrètes et de l'informatique fondamentale avec la biologie moléculaire et la médecine.
Organisateurs de la journée : Jean Krivine, Paul Ruet
Accès : Bâtiment Sophie Germain, 8 place Aurélie Nemours, 75013 Paris
|9:30 - 10:00||Accueil|
|10:00 - 10:15||Présentation du groupe de travail Bioss|
|10:15 - 11:15||Christine Brun (keynote talk) - Interactomes of multifunctional proteins|
|11:15 - 11:30||Café - mise en place Session 1 exposés courts|
|11:30 - 11:45||Sucheendra Palaniappan - Approximating the dynamics of the Hybrid Stochastic-Deterministic Apoptosis pathway|
|11:45 - 12:00||Adrien Rougny - Two Qualitative Dynamics Semantics for SBGN Process Description Maps|
|12:00 - 12:15||François Fages - Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications|
|12:15 - 12:30||Nathalie Théret - Microenvironment and activation of TGF-β|
|12:30 - 14:00||Déjeuner|
|14:00 - 15:00||Oded Maler (keynote talk) - Dynamical Systems Biology|
|15:00 - 15:15||Café - mise en place Session 2 exposés courts|
|15:15 - 15:30||Vincent Danos - Models of growth|
|15:30 - 15:45||Jérôme Feret - Une approche algébrique pour détecter et utiliser les symmétries d'un modèle basé sur des règles de récriture|
|15:45 - 16:00||Adrien Basso-Blandin - A knowledge representation meta-model for rule-based modelling of signalling networks|
|16:00 - 16:15||Loïc Paulevé - Abstractions pour la dynamique des réseaux qualitatifs|
|16:15 - 16:30||Paul Ruet - Negative local feedbacks in Boolean networks|
|16:30 - 16:45||Adrien Richard - Simple dynamics on graphs|
|16:45 - 17:00||Café - mise en place Session 3 exposés courts|
|17:00 - 17:15||Célia Biane - Interaction Network Game applied to drug prediction in precision medicine|
|17:15 - 17:30||Gautier Stoll - MaBoSS tool : modeling signaling network in a Boolean framework with continuous time. Principles and applications|
|17:30 - 17:45||Vincent Picard - Analyse stationnaire des réseaux de réactions : systèmes de contraintes en modélisation stochastique|
|17:45 - 18:00||Virgile Andreani - A Stochastic Model of Metabolism and Growth|
Virgile Andreani -
A Stochastic Model of Metabolism and Growth
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 . 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.  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.  Kiviet et al. Stochasticity of metabolism and growth at the single-cell level, Nature, 2014, 514:376-379  Weiße et al. Mechanistic links between cellular trade-offs, gene expression, and growth, PNAS, 2015, 112(9):E1038-E1047
Adrien Basso-Blandin -
A knowledge representation meta-model for rule-based modelling of signalling networks
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. Cet exposé décrira formellement le modèle de représentation de connaissances présenté lors du précédent gt et introduira les notions d'analyses sémantiques que nous développons à l'heure actuelle.
Célia Biane -
Interaction Network Game applied to drug prediction in precision medicine
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.
Christine Brun -
Interactomes of multifunctional proteins (Keynote talk)
Vincent Danos -
Models of growth
François Fages -
Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications
Jérôme Feret -
Une approche algébrique pour détecter et utiliser les symmétries d'un
modèle basé sur des règles de récriture
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.
Oded Maler -
Dynamical Systems Biology (Keynote talk)
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.
Sucheendra Palaniappan - Approximating the dynamics of the Hybrid Stochastic-Deterministic Apoptosis pathway
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.
Loïc Paulevé -
Abstractions pour la dynamique des réseaux qualitatifs
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...
Vincent Picard -
Analyse stationnaire des réseaux de réactions : systèmes de contraintes en modélisation stochastique
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.
Adrien Richard -
Simple dynamics on graphs
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).
Adrien Rougny -
Two Qualitative Dynamics Semantics for SBGN Process Description Maps
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.
Paul Ruet -
Negative local feedbacks in Boolean networks
Gautier Stoll -
MaBoSS tool : modeling signaling network in a Boolean framework with continuous time. Principles and applications
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.
Nathalie Théret -
Microenvironment and activation of TGF-β
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.