Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.7/897
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dc.contributor.authorMendes, Nuno D.-
dc.contributor.authorHenriques, Rui-
dc.contributor.authorRemy, Elisabeth-
dc.contributor.authorCarneiro, Jorge-
dc.contributor.authorMonteiro, Pedro T.-
dc.contributor.authorChaouiya, Claudine-
dc.identifier.citationMendes ND, Henriques R, Remy E, Carneiro J, Monteiro PT and Chaouiya C (2018) Estimating Attractor Reachability in Asynchronous Logical Models. Front. Physiol. 9:1161. doi: 10.3389/fphys.2018.01161pt_PT
dc.descriptionThis deposit is composed by the main article. The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2018.01161/full#supplementary-material.pt_PT
dc.description.abstractLogical models are well-suited to capture salient dynamical properties of regulatory networks. For networks controlling cell fate decisions, cell fates are associated with model attractors (stable states or cyclic attractors) whose identification and reachability properties are particularly relevant. While synchronous updates assume unlikely instantaneous or identical rates associated with component changes, the consideration of asynchronous updates is more realistic but, for large models, may hinder the analysis of the resulting non-deterministic concurrent dynamics. This complexity hampers the study of asymptotical behaviors, and most existing approaches suffer from efficiency bottlenecks, being generally unable to handle cyclical attractors and quantify attractor reachability. Here, we propose two algorithms providing probability estimates of attractor reachability in asynchronous dynamics. The first algorithm, named Firefront, exhaustively explores the state space from an initial state, and provides quasi-exact evaluations of the reachability probabilities of model attractors. The algorithm progresses in breadth, propagating the probabilities of each encountered state to its successors. Second, Avatar is an adapted Monte Carlo approach, better suited for models with large and intertwined transient and terminal cycles. Avatar iteratively explores the state space by randomly selecting trajectories and by using these random walks to estimate the likelihood of reaching an attractor. Unlike Monte Carlo simulations, Avatar is equipped to avoid getting trapped in transient cycles and to identify cyclic attractors. Firefront and Avatar are validated and compared to related methods, using as test cases logical models of synthetic and biological networks. Both algorithms are implemented as new functionalities of GINsim 3.0, a well-established software tool for logical modeling, providing executable GUI, Java API, and scripting facilities.pt_PT
dc.description.sponsorshipThis work was supported by Fundação para a Ciência e a Tecnologia (FCT, Portugal), grants PTDC/EIA-CCO/099229/2008, UID/CEC/50021/2013, IF/01333/2013, and PTDC/EEI-CTP/2914/2014.pt_PT
dc.publisherFrontiers Mediapt_PT
dc.subjectregulatory networkpt_PT
dc.subjectlogical modelingpt_PT
dc.subjectdiscrete asynchronous dynamicspt_PT
dc.titleEstimating Attractor Reachability in Asynchronous Logical Modelspt_PT
degois.publication.titleFrontiers in Physiologypt_PT
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