Section: New Results

Model checking of Logical Biological Models

Model checking logical regulatory networks

Participants : Pedro T. Monteiro [INESC-ID, Lisboa, Portugal] , Wassim Abou-Jaoudé, Denis Thieffry [IBENS, France] , Claudine Chaouiya [IGC, Oeiras, Portugal] .

Model checking, Regulatory networks. Regulatory and signalling networks control cell behaviours in response to environmental cues. The logical formalism has been widely employed to study these interaction networks, which are modelled as discrete dynamical systems. While biologists identify networks encompassing more and more components, properties of biological relevance become hard to verify.

In [22] , we report on the use of model-checking techniques to address this challenge. This approach is illustrated by an application dealing with the modelling of T-helper lymphocyte differentiation.

Model checking to assess T-helper cell plasticity

Participants : Wassim Abou-Jaoudé, Pedro T. Monteiro [INESC-ID, Lisboa, Portugal] , Aurélien Naldi [Centre Intégratif de Lausanne, Lausanne, Switzerland] , Maximilien Grandclaudon [Institut Curie, Paris, France] , Vassili Sommeils [Institut Curie, Paris, France] , Claudine Chaouiya [IGC, Oeiras, Portugal] , Denis Thieffry [IBENS, France] .

Model checking, Logical modeling. Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states).The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models.We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present, in [8] , an extended version of a published model of Th cell differentiation.We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.