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Estimating Attractor Reachability in Asynchronous Logical Models.

Nuno D Mendes1, Rui Henriques2,3, Elisabeth Remy4

  • 1Instituto Gulbenkian de Ciência, Oeiras, Portugal.

Frontiers in Physiology
|September 25, 2018
PubMed
Summary
This summary is machine-generated.

We developed two new algorithms, Firefront and Avatar, to estimate the probability of reaching attractors in asynchronous regulatory network models. These methods improve the analysis of complex biological systems and cell fate decisions.

Keywords:
attractorsdiscrete asynchronous dynamicslogical modelingreachabilityregulatory network

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Dynamics

Background:

  • Logical models capture regulatory network dynamics, with attractors representing cell fates.
  • Asynchronous updates are more realistic than synchronous ones but complicate dynamic analysis.
  • Existing methods struggle with large models, cyclical attractors, and quantifying reachability.

Purpose of the Study:

  • To develop efficient algorithms for estimating attractor reachability probabilities in asynchronous logical models.
  • To address limitations of existing methods in handling complex dynamics and large-scale networks.
  • To provide tools for analyzing cell fate decisions and network behaviors.

Main Methods:

  • Firefront: Exhaustive state-space exploration with breadth-first probability propagation.
  • Avatar: Adapted Monte Carlo approach using random walks to estimate reachability, avoiding transient cycles.
  • Validation using synthetic and biological logical models within GINsim 3.0.

Main Results:

  • Firefront provides quasi-exact reachability probability estimates.
  • Avatar effectively handles large models with complex cyclic attractors.
  • Both algorithms outperform existing methods in efficiency and scope.

Conclusions:

  • Firefront and Avatar offer novel solutions for analyzing asynchronous logical models.
  • These algorithms enhance the study of dynamical properties, including cell fate decisions.
  • The methods are integrated into GINsim 3.0, providing accessible computational tools.