Approximating attractors of Boolean networks by iterative CTL model checking

This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: faithfulness which requires that the oscillating variables of all attractors in a trapspace correspond to th...

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Bibliographic Details
Main Authors: Hannes eKlarner, Heike eSiebert
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-09-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00130/full
Description
Summary:This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: faithfulness which requires that the oscillating variables of all attractors in a trapspace correspond to their dimensions, univocality which requires that there is a unique attractor in each trap space and completeness which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal and complete which suggests that they are in general good approximations for the asymptotics of Boolean networks.
ISSN:2296-4185