Please use this identifier to cite or link to this item: http://hdl.handle.net/10400.7/897
Title: Estimating Attractor Reachability in Asynchronous Logical Models
Author: Mendes, Nuno D.
Henriques, Rui
Remy, Elisabeth
Carneiro, Jorge
Monteiro, Pedro T.
Chaouiya, Claudine
Keywords: regulatory network
logical modeling
discrete asynchronous dynamics
attractors
reachability
Issue Date: 7-Sep-2018
Publisher: Frontiers Media
Citation: Mendes 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.2018.01161
Abstract: Logical 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.
Description: This 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.
Peer review: yes
URI: http://hdl.handle.net/10400.7/897
DOI: 10.3389/fphys.2018.01161
Publisher Version: https://www.frontiersin.org/articles/10.3389/fphys.2018.01161/full
Appears in Collections:QOB - Artigos
NM- Artigos

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