A Novel Algorithm for the Maximal Fit Problem in Boolean Networks

TL;DR

This paper introduces a new sampling-based algorithm for fitting Boolean network models to gene expression data, capable of handling large, noisy datasets and applicable to various network types.

Contribution

The novel algorithm improves fitting accuracy and scalability for Boolean networks, addressing limitations of previous heuristic and specialized methods.

Findings

Effective on large, noisy datasets

Handles time series and steady state data

Applicable to various network types

Abstract

Gene regulatory networks (GRNs) are increasingly used for explaining biological processes with complex transcriptional regulation. A GRN links the expression levels of a set of genes via regulatory controls that gene products exert on one another. Boolean networks are a common modeling choice since they balance between detail and ease of analysis. However, even for Boolean networks the problem of fitting a given network model to an expression dataset is NP-Complete. Previous methods have addressed this issue heuristically or by focusing on acyclic networks and specific classes of regulation functions. In this paper we introduce a novel algorithm for this problem that makes use of sampling in order to handle large datasets. Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise.