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Model Checking to Assess T-Helper Cell Plasticity

dc.contributor.authorAbou-Jaoudé, Wassim
dc.contributor.authorMonteiro, Pedro T.
dc.contributor.authorNaldi, Aurélien
dc.contributor.authorGrandclaudon, Maximilien
dc.contributor.authorSoumelis, Vassili
dc.contributor.authorChaouiya, Claudine
dc.contributor.authorThieffry, Denis
dc.date.accessioned2015-10-12T16:25:39Z
dc.date.available2015-10-12T16:25:39Z
dc.date.issued2015-01-28
dc.description.abstractComputational 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.pt_PT
dc.description.sponsorshipLabEx MemoLife, Ecole Normale Supérieure, FCT grants: (PEst-OE/EEI/LA0021/2013, IF/01333/2013), Ph.D.program of the Agence National de Recherche sur Le Sida (ANRS), European Research Council consolidator grant.pt_PT
dc.identifier10.3389/fbioe.2014.00086
dc.identifier.citationAbou-Jaoudé W, Monteiro PT, Naldi A, Grandclaudon M, Soumelis V, Chaouiya C and Thieffry D (2015) Model checking to assess T-helper cell plasticity. Front. Bioeng. Biotechnol. 2:86. doi: 10.3389/fbioe.2014.00086pt_PT
dc.identifier.doi10.3389/fbioe.2014.00086
dc.identifier.urihttp://hdl.handle.net/10400.7/388
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherFrontiers Research Foundationpt_PT
dc.relation.publisherversionhttp://journal.frontiersin.org/article/10.3389/fbioe.2014.00086/abstractpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectlogical modelingpt_PT
dc.subjectsignaling networkspt_PT
dc.subjectT-helper lymphocytept_PT
dc.subjectcell differentiationpt_PT
dc.subjectcell plasticitypt_PT
dc.subjectmodel checkingpt_PT
dc.titleModel Checking to Assess T-Helper Cell Plasticitypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleFrontiers in Bioengineering and Biotechnologypt_PT
oaire.citation.volume2pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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