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A Probabilistic Approach to Study Yeast’s Gene Regulatory Network | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

A Probabilistic Approach to Study YeastÂ’s Gene Regulatory Network

Pinto F.R*
Centro de Química e Bioquímica, Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
Corresponding Author : Pinto F.R
Centro de Química e Bioquímica
Departamento de Química e Bioquímica
Faculdade de Ciências da Universidade de Lisboa
Campo Grande, 1749-016 Lisboa, Portugal
Phone : +351 217500891
Fax : +351 217500088
Email : [email protected]
Received December 14, 2008; Accepted February 25, 2009; Published February 27, 2009
Citation: Pinto F.R (2009) A Probabilistic Approach to Study Yeast’s Gene Regulatory Network. J Comput Sci Syst Biol 2:044-050. doi:10.4172/jcsb.1000016
Copyright: © 2009 Pinto F.R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Using only the transcription network structure information, a probabilistic model was developed that computes the probabilities with which a pair of genes responds simultaneously (SR) or differentially (DR) to a random network perturbation. Study of yeast’s transcription regulatory network in association with gene expression profiles shows that SR and DR probabilities are significantly associated with the distribution of strong co-expression. It is 100 fold more probable to observe co-expression when P(SR)»0.5 for a random perturbation of 3 transcription factors (TFs), allowing for perturbation spread until a depth of 3 connections in the regulatory network. The model also predicts that positive co-expression enhancement is related with the proportion of common TFs (number of TFs that regulate both genes in a pair divided by the total number of TFs that regulate at least one gene in the pair), and not to the absolute number. The relationship between the model derived probabilities and other graph-theoretic measures used to analyse biological networks is discussed.


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