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Front Bioeng Biotechnol. 2015 Jan 28;2:86. doi: 10.3389/fbioe.2014.00086. eCollection 2014.

Model checking to assess T-helper cell plasticity.

Author information

1
Institut de Biologie de l'Ecole Normale Supérieure , Paris , France ; UMR CNRS 8197 , Paris , France ; INSERM U1024 , Paris , France ; Laboratoire d'Informatique de l'Ecole Normale Supérieure , Paris , France.
2
INESC-ID , Lisboa , Portugal ; Instituto Gulbenkian de Ciência , Oeiras , Portugal.
3
Centre Intégratif de Génomique, Université de Lausanne , Lausanne , Switzerland.
4
Laboratoire d'Immunologie Clinique, Institut Curie , Paris , France ; INSERM U932 , Paris , France.
5
Instituto Gulbenkian de Ciência , Oeiras , Portugal.
6
Institut de Biologie de l'Ecole Normale Supérieure , Paris , France ; UMR CNRS 8197 , Paris , France ; INSERM U1024 , Paris , France.

Abstract

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 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.

KEYWORDS:

T-helper lymphocyte; cell differentiation; cell plasticity; logical modeling; model checking; signaling networks

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