Probabilistic representation of gene regulatory networks.
BACKGROUND
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.


BACKGROUND
The simulation software is available upon request.


BACKGROUND
Supplementary material will be made available on the OUP server.
Mao
Linyong
L
Computational Biosciences Group, Pacific Northwest National Laboratory, PO Box 999, Mail Stop K1-92, Richland, WA 99352, USA.
Resat
Haluk
H
D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes; D014157:Transcription Factors / Q000502:physiology*
D014157:Transcription Factors