Probabilistic representation of gene regulatory networks

Authors:
Linyong Mao;Haluk Resat
Affiliations:
Computational Biosciences Group, Pacific Northwest National Laboratory, PO Box 999, Mail Stop K1-92, Richland, WA 99352, USA;Computational Biosciences Group, Pacific Northwest National Laboratory, PO Box 999, Mail Stop K1-92, Richland, WA 99352, USA
Venue:
Bioinformatics
Year:
2004

Citing 0
Cited 2

A compositional approach to the stochastic dynamics of gene networks

Transactions on Computational Systems Biology IV
A compositional approach for modeling and simulation of bio-molecular systems

Proceedings of the Winter Simulation Conference

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Abstract

Motivation: Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. Results: In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. Availability: The simulation software is available upon request. Supplementary information: Supplementary material will be made available on the OUP server.