Assembling biological boolean networks using manually curated databases and prediction algorithms

TL;DR

This paper presents a method for automatically assembling Boolean networks from biological databases and algorithms, enabling the analysis of dynamical systems, steady states, and stimulus responses in biological research.

Contribution

It introduces a novel approach combining curated databases and prediction algorithms to construct Boolean networks for biological systems.

Findings

Automated generation of Boolean networks from biological data

Ability to predict steady states and stimuli responses

Integration of protein interaction data with computational algorithms

Abstract

Despite the large quantity of information available, thorough researches in various biological databases are still needed in order to reconstruct and understand the steps that lead to known or new phenomena. By using protein-protein interaction networks and algorithms to extract relevant interconnections among proteins of interest, it is possible to assemble subnetworks from global interactomes. Using these extracted networks it is possible to use algorithms to predict signal directions while activation and inhibition effects can be predicted using RNA interference screenings. The result of this approach is the automatic generation of boolean networks. This way of modelling dynamical systems allows the discovery of steady states and the prediction of stimuli response.