Computing preimages of Boolean Networks

TL;DR

This paper introduces a linear-time probabilistic algorithm for computing preimages in feed-forward Boolean networks, with evaluations on random networks and E. coli's regulatory network.

Contribution

It presents a novel probabilistic approach that efficiently solves the predecessor problem in Boolean networks, improving over existing methods.

Findings

Algorithm runs in linear time relative to network size

Effective on both random and biological networks

Demonstrates scalability and practical applicability

Abstract

In this paper we present an algorithm to address the predecessor problem of feed-forward Boolean networks. We propose an probabilistic algorithm, which solves this problem in linear time with respect to the number of nodes in the network. Finally, we evaluate our algorithm for random Boolean networks and the regulatory network of Escherichia coli.