Section: New Results

Asymptotic dynamics

Participants : Anne Siegel, Oumarou Abdou-Arbi, Geoffroy Andrieux, Jérémie Bourdon, Jeanne Cambefort, Damien Eveillard, Michel Le Borgne, Vincent Picard, Sven Thiele, Santiago Videla.

Learning families of boolean signaling networks We propose the use of ASP to explore the space of feasible logic models of a signaling network. To that matter, we exhaustively enumerate the set of sub-optimal boolean logical models which are compatible with both the topology of a knowledge-based influence graph and the observed response of the system to several perturbations (phosphorylation datasets). We illustrate the importance of characterizing such a family of models in a global and exhaustive manner by revisiting a model of pro-growth and inflammatory pathways in human liver cells and studying the variability with the set of compatible models. [A. Siegel, S. Thiele, S. Videla] [18] [Online publication]

Control the steady-state response of qualitative signaling networks: intervention sets The minimal intervention set problem roughly consists in identifying the perturbation that can be undergone over a signaling network to predict a fixed expected behavior. We have provided a precise characterization of the minimal intervention set problem relying on three-valued logic and fixpoint semantics. We address this problem within ASP and using real-world biological benchmarks we show that it greatly outperforms previous work using dedicated algorithms. [A. Siegel, S. Videla] [19] [Online publication]

Reachability in dynamical signaling networks: cut sets In the scope of discrete finite-state models of interacting components, we present a novel algorithm for identifying sets of local states of components whose activity is necessary for the reachability of a given local state. Those sets are referred to as cut sets; they provide potential therapeutic targets that are proven to prevent molecules of interest to become active, up to the correctness of the model. Our method is based on the so-called Graph of Local Causality and form an under-approximation of the complete minimal cut sets of the dynamics. It makes tractable the formal analysis of very large scale networks. [G. Andrieux, M. Le Borgne] [28] , [12] [Online publication] [Thesis]

Exploring metabolism flexibility through quantitative study of precursor sets for system outputs We extended a Flux-Balanced-Analysis approach to quantify the precursor composition of each system output and to discuss the biological relevance of a set of flux in a given metabolic network. The composition is called contribution of inputs over outputs [AIO]. In order to further investigate metabolic network flexibility, we have proposed an efficient local search algorithm computing the extremal values of AIO coefficients. This approach enables to discriminate diets without making any assumption on the internal behaviour of the system. [J. Bourdon, O. Abdou-Arbi, A. Siegel] [15] , [11] [Thesis]