Coarse-grain reconstruction of genetic networks from expression levels

L. Diambra

Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, issue 11, 2198-2207

Abstract: In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at a coarse-grain level from high throughput gene expression data. The method provides the a posteriori probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to choose those genes that will be perturbed in subsequent experiments in order to refine our knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by in numero experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as recovering a small subnetwork of interest by performing perturbation over selected genes.

Keywords: Gene network; Reverse engineering; Information theory (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:390:y:2011:i:11:p:2198-2207

DOI: 10.1016/j.physa.2011.02.021

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