Contributions à l’élaboration de connaissances qualitatives en bio-informatique

This habilitation thesis gathers several works in the field of formal methods for biology. In our works, we tackled the challenge of modeling and analyzing the dynamics of large-scale biological regulatory networks. To address this issue, we identified some relevant class of formal models on which it is possible to perform effective analysis of its dynamics. After discussing the different modeling criteria to be taken into account in biology, we introduce the Process Hitting framework. We then present the methods that we designed to analyze such models, and their respective merits and limitats. Finally, we give an overview of recent research aiming to build a fruitful link between machine learning, logic programming, model-checking and bioinformatics. This allows us to bring out a new set of scientific questions.Cette synthèse pour l’HDR est un recueil de plusieurs travaux dans le domaine des méthodes formelles pour la biologie. Devant l’enjeu que représente la modélisation et l’analyse de la dynamique de réseaux de régulation biologiques à grande échelle, nous avons identifié une classe pertinente de modèles formels sur laquelle il est possible de mener des analyses efficaces de la dynamique. Après avoir discuté les différents critères de modélisation à prendre en compte en biologie, nous introduisons ainsi le formalisme des Frappes de Processus. Nous présentons ensuite les méthodes d’analyse conçues pour ce paradigme, leurs mérites et leurs limites. Enfin, nous revenons sur des résultats plus récents, consécutifs à l’établissement de liens fructueux entre l’apprentissage automatique, la programmation logique, le model-checking et la bio-informatique, ce qui nous permet de faire émerger un ensemble de nouvelles questions scientifiques

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks

Modeling and simulation of molecular systems helps in understanding the behavioral mechanism of biological regulation. Time delays in production and degradation of expressions are important parameters in biological regulation. Constraints on time delays provide insight into the dynamical behavior of a Biological Regulatory Network (BRN). A recently introduced Process Hitting (PH) Framework has been found efficient in static analysis of large BRNs, however, it lacks the inference of time delays and thus determination of their constraints associated with the evolution of the expression levels of biological entities of BRN is not possible. In this paper we propose a Hybrid Process Hitting scheme for introducing time delays in Process Hitting Framework for dynamical modeling and analysis of Large Biological Regulatory Networks. It provides valuable insights into the time delays corresponding to the changes in the expression levels of biological entities thus possibly helping in identification of therapeutic targets. The proposed framework is applied to a well-known BRNs of Bacteriophage λ and ERBB Receptor-regulated G1/S transition involved in the breast cancer to demonstrate the viability of our approach. Using the proposed approach, we are able to perform goal-oriented reduction of the BRN and also determine the constraints on time delays characterizing the evolution (dynamics) of the reduced BRN

Sufficient Conditions for Reachability in Automata Networks with Priorities

International audienceIn this paper, we develop a framework for an efficient under-approximation of the dynamics of Asynchronous Automata Networks (AANs). An AAN is an Automata Network with synchronised transitions between automata, where each transition changes the local state of exactly one automaton (but any number of synchronizing local states are allowed). The work we propose here is based on static analysis by abstract interpretation, which allows to prove that reaching a state with a given property is possible, without the same computational cost of usual model checkers: the complexity is polynomial with the total number of local states and exponential with the number of local states within a single automaton. Furthermore, we address AANs with classes of priorities, and give an encoding into AANs without priorities, thus extending the application range of our under-approximation. Finally, we illustrate our method for the model checking of large-scale biological networks