Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Carlos Ha Higa,Vitor Hp Louzada,Tales P Andrade,Ronaldo F Hashimoto

doi:10.1186/1753-6561-5-s2-s5

Carlos Ha Higa, Vitor Hp Louzada + Show 2 more

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https://doi.org/10.1186/1753-6561-5-s2-s5

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Abstract

BackgroundA popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it.ResultsWe applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.ConclusionsThe algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Highlights

A popular model for gene regulatory networks is the Boolean network model
In order to analyze the gene interactions, we have generated several gene connections in consistent networks by using Constraint Satisfaction Problem (CSP) solver techniques which in turn utilized constraints sets built from three algorithms provided by this work
We have applied our methodology to an artificial dataset that had been generated by a Boolean network that models the budding yeast cell cycle [35], and to an experimental dataset of HeLa cells [36]

Summary

Introduction

A popular model for gene regulatory networks is the Boolean network model. One of the goals of Systems Biology is to study the various cellular mechanisms and components In many cases these mechanisms are complex, where some of the interactions between the proteins are still unknown. To represent these interactions it is common to use gene regulatory networks (GRN). The simplest discrete model was introduced by Kauffman [1] and its known as Boolean network. Later, this model was modified to express uncertainty giving rise to the probabilistic Boolean network [2,3]. Friedman introduced Bayesian networks[4] as a probabilistic tool for the identification of regulatory data and showed that they can reproduce

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