**1 et 2 juillet 2016**à Lyon

### Informations générales

Date : 1er et 2 juillet 2016

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

Plan d'accès
ici

Organisateurs : Olivier Gandrillon, Cédric Lhoussaine, Élisabeth Remy, Sylvain Sené et Anne Siegel

La deuxième édition des journées annuelles du GT Bioss va se dérouler à la suite des Journées ouvertes de biologie, informatique et mathématiques (JOBIM) organisées par la Société française de bio-informatique (SFBI). Ainsi, les 1er et 2 juillet 2016, les membres du GT auront le plaisir de se rencontrer autour de conférences autour des thèmes suivants :\

- la modélisation stochastique en biologie ;\
- la régulation génétique ;\
- le métabolisme.

### Inscription

L'inscription, gratuite mais obligatoire, se fait en remplissant le formulaire accessible ici.

### Orateurs invités

Grégory BATT, INRIA Saclay

Marcelline KAUFMAN, Université libre de Bruxelles

Marie-France SAGOT, INRIA Lyon\

### Programme

#### Vendredi 1er juillet

**09h00 - 09h30** - **Accueil****09h30 - 09h45** - **Introduction des journées****09h45 - 10h30** - **Conférence plénière** - Grégory Batt - *Predicting
long-term effects of apoptosis-inducing drug treatments: coupling signal
transduction pathways with stochastic protein turnover models***10h30 - 11h00** - Bertrand Miannay - *Identification des voies de
signalisation impliquées dans le myélome multiple par programmation par
contrainte***11h00 - 11h30** - Arnaud Bonnaffoux - *Toward a dynamic
multi-scale/level approach for gene regulatory network inference***11h30 - 12h00** - Nicolas Schabanel - *Folding Turing is hard but
feasible***12h00 - 13h30** - **Pause déjeuner****13h30 - 14h15** - **Conférence plénière** - Marie-France Sagot -
*Species interactions from a metabolism perspective***14h15 - 14h45** - Nils Giordano - *Dynamical allocation of cellular
resources as an optimal control problem***14h45 - 15h15** - Victorien Delannée - *A modeling approach to
evaluate the balance between bioactivation and detoxification of MeIQx
in human hepatocytes***15h15 - 15h45** - **Discussion Bioss / GDR****15h45 - 16h15** - **Pause****16h15 - 16h45** - Hugues Berry - *Estimating the effects of spatial
non-homogeneities in intracellular diffusion-reactions***16h45 - 17h15** - Dan Goreac - *Hybrid designing using stochastic
backward equations***17h15 - 17h45** - Guillaume Madelaine - *Structural simplifications of
reaction networks: the confluence problem***17h45 - 18h15** - Ferdinanda Camporesi - *Context-sensitive flow
analyses: a hierarchy of model reductions*\

#### Samedi 2 juillet

**09h00 - 09h45** - **Conférence plénière** - Marcelline Kaufman - *On
multistationarity in chemical reaction networks***09h45 - 10h15** - Kévin Perrot - *On the flora of asynchronous locally
non-monotonic Boolean networks***10h15 - 10h45** - Élisabeth Remy - *Discrete dynamics of compound
regulatory circuits***10h45 - 11h00** - **Pause****11h00 - 11h30** - Loïc Paulevé - *Around reachability in automata
networks***11h30 - 12h00** - Emna Ben Abdallah - *Inference of biological
regulatory networks from time series data***12h00 - 12h30** - Adrien Richard - *Points fixes dans les réseaux
booléens monotones*\

### Résumés

**Grégory Batt** - *Predicting long-term effects of apoptosis-inducing
drug treatments: coupling signal transduction pathways with stochastic
protein turnover models*

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.**Emna Ben Abdallah** - *Inference of biological regulatory networks
from time series data*

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.

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.

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.**Hugues Berry** - *Estimating the effects of spatial non-homogeneities
in intracellular diffusion-reactions*

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.**Arnaud Bonnaffoux** - *Toward a dynamic multi-scale/level approach for
gene regulatory network inference*

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:\

- 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)\
- Model reduction. This is mostly an ongoing work, and will make use of specific constraints applying to the network.\
- The final step will consist in network inference constrained by
dynamic multi-scale/level observations.
**Ferdinanda Camporesi**-*Context-sensitive flow analyses: a hierarchy of model reductions*

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.**Victorien Delannée**-*A modeling approach to evaluate the balance between bioactivation and detoxification of MeIQx in human hepatocytes*

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.

Our results demonstrate that 1) the detoxification pathway predominates,

- 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.
**Nils Giordano**-*Dynamical allocation of cellular resources as an optimal control problem: novel insights into microbial growth strategies*

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.**Dan Goreac**-*Hybrid designing using stochastic backward equations*

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.**Marcelline Kaufman**-*On multistationarity in chemical reaction networks*

Résumé au format pdf ici.**Guillaume Madelaine**-*Structural simplifications of reaction networks: the confluence problem*

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.**Bertrand Miannay**-*Identification des voies de signalisation impliquées dans le myélome multiple par programmation par contrainte*

Résumé au format pdf ici.**Loïc Paulevé**-*Around reachability in automata networks*

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.**Kévin Perrot**-*On the flora of asynchronous locally non-monotonic Boolean networks*

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.**Élisabeth Remy**-*Discrete dynamics of compound regulatory circuits*

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.**Adrien Richard**-*Points fixes dans les réseaux booléens monotones*

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.

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 ?

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.

C'est un travail réalisé en collaboration avec Julio Aracena et Lilian Salinas (Université de Concepcion, Chili) disponible à l'adresse suivante: http://arxiv.org/abs/1602.03109.**Marie-France Sagot**-*Species interactions from a metabolism perspective*

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.**Nicolas Schabanel**-*Folding Turing is hard but feasible*

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.

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.\

### Participants

Jalouli ACHREF, Université de Limoges

Vicente ACUNA, CMM, Santiago, Chili

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, 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 et biologie théorique,
Université libre de Bruxelles

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

Arnaud PORET, LBMC, ÉNS-Lyon

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

Dernière modification le 01/07/2016