BioSysBio:abstracts/2007/Le Yu

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Inferring Context-Sensitive Probabilistic Boolean Networks from Gene Expression Data Under Multi-Biological Conditions

Author(s): Le Yu and Stephen Marshall
Affiliations: University of Strathclyde
Contact: l.yu@eee.strath.ac.uk
Keywords: 'gene regulatory networks' 'boolean network' 'probabilistic boolean network' 'attractor'

Abstract

There is an increasing need to develop frameworks for the formal analysis of biological pathways. This report addresses a new modeling approach for analyzing temporal data sequences of gene expression using Probabilistic Boolean Networks. The modeling is applied in the context of interferon pathway biology to the analysis of gene interaction networks. 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 particularly amenable to probabilistic modeling and accordingly, 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. The model is a collection of traditional Probabilistic Boolean Networks. We further investigate and analyse the length of the attractor basin in the Boolean Networks inferred from the data, and identify the network selection probablities of the model according to the frequencey distribution of the observed gene expression data.

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References

Links

Le Yu [ http://www.eee.strath.ac.uk/~polaris/ ]


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