A SAT-Based Algorithm for Computing Attractors in Synchronous Boolean Networks

TL;DR

This paper introduces a SAT-based algorithm for efficiently computing attractors in synchronous Boolean networks, which are crucial for modeling gene regulatory networks and understanding cell types.

Contribution

The paper presents a novel SAT-based bounded model checking algorithm with a termination condition that avoids computing the network diameter and reduces clause complexity.

Findings

Uses less space than BDD-based approaches

Handles larger networks efficiently

Potential for significant scalability improvements

Abstract

This paper addresses the problem of finding cycles in the state transition graphs of synchronous Boolean networks. Synchronous Boolean networks are a class of deterministic finite state machines which are used for the modeling of gene regulatory networks. Their state transition graph cycles, called attractors, represent cell types of the organism being modeled. When the effect of a disease or a mutation on an organism is studied, attractors have to be re-computed every time a fault is injected in the model. We present an algorithm for finding attractors which uses a SAT-based bounded model checking. Novel features of the algorithm compared to the traditional SAT-based bounded model checking approaches are: (1) a termination condition which does not require an explicit computation of the diameter and (2) a technique to reduce the number of additional clauses which are needed to make paths loop-free. The presented algorithm uses much less space than existing BDD-based approaches and has a potential to handle several orders of magnitude larger networks.