Boolean dynamics of genetic regulatory networks inferred from microarray time series data
Open Access
- 31 January 2007
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 23 (7), 866-874
- https://doi.org/10.1093/bioinformatics/btm021
Abstract
Motivation: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. Results: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. Contact:jfaulon@sandia.gov Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 33 references indexed in Scilit:
- Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networksSignal Processing, 2006
- Stable and unstable attractors in Boolean networksPhysical Review E, 2005
- Reverse engineering of regulatory networks in human B cellsNature Genetics, 2005
- Inferences, questions and possibilities in Toll-like receptor signallingNature, 2004
- Superpolynomial Growth in the Number of Attractors in Kauffman NetworksPhysical Review Letters, 2003
- Modeling and Simulation of Genetic Regulatory Systems: A Literature ReviewJournal of Computational Biology, 2002
- Coupled two-way clustering analysis of gene microarray dataProceedings of the National Academy of Sciences of the United States of America, 2000
- Using Bayesian Networks to Analyze Expression DataJournal of Computational Biology, 2000
- Counting and Classifying Attractors in High Dimensional Dynamical SystemsJournal of Theoretical Biology, 1996
- Metabolic stability and epigenesis in randomly constructed genetic netsJournal of Theoretical Biology, 1969