Parameter estimation for Boolean models of biological networks

TL;DR

This paper introduces Polynome, a web-based software tool that facilitates the construction and parameter estimation of Boolean network models from experimental biological data, aiding reverse-engineering of molecular networks.

Contribution

It presents a novel software package for parameter estimation in Boolean models, enabling reverse-engineering from experimental data in systems biology.

Findings

Polynome effectively reconstructs Boolean networks from time course data.

The software provides a discrete analog of parameter estimation for biological networks.

It supports users in modeling complex molecular interactions.

Abstract

Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data.