Inferring context-sensitive Probabilistic Boolean Networks from gene expression data under multi-biological conditions

Le Yu,Stephen Marshall

doi:10.1186/1752-0509-1-s1-p63

Abstract

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions.

Highlights

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics
Based on the analysis of gene expression measurements of macrophage cells challenged with virus infection and interferon treatment, we demonstrate that switch-like phenomena exists
The switch like responses are amenable to probabilistic modeling and we develop a new model extending the Probabilistic Boolean Networks (PBNs) concept for the inference of gene regulatory networks from gene expression time-course data under different biological conditions

Summary

Background

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. There is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as PinrfoebrraibnigligsteinceBtiocorleegaunlaNtoertwy noerktws o(PrkBsN[s1)].wTahsisptreocphonsoeldofgoyr, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions. Requirement of contextual data consistency [3], and identify the network selection probabilities of the model according to the frequencey distribution of the observed gene expression data

Methods and materials

Results

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