A reduction method for noisy Boolean networks

TL;DR

This paper introduces a method to simplify noisy Boolean networks by reducing them to a Markov chain with states corresponding to the network's attractors, facilitating analysis of their long-term behavior.

Contribution

It proposes a novel reduction technique that maps noisy Boolean networks to coarse-grained Markov chains based on attractors, enabling easier study of their dynamics.

Findings

Effective reduction of noisy Boolean networks to Markov chains.

Preservation of attractor-based dynamics in the reduced model.

Facilitation of long-term behavior analysis.

Abstract

This paper is concerned with the reduction of a noisy synchronous Boolean network to a coarse-grained Markov chain with number of states being equal to the number of attractors of the original network.