Boolean dynamics of genetic regulatory networks inferred from microarray time series data
Abstract
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 paper 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. 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. In conclusion, 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.
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biology Dept.
- Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biosystems Research
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Biomolecular Analysis and Imaging
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biosciences Dept.
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); SNL Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1427005
- Report Number(s):
- SAND2007-0437J
Journal ID: ISSN 1367-4803; 524009
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Bioinformatics
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 7; Journal ID: ISSN 1367-4803
- Publisher:
- Oxford University Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION
Citation Formats
Martin, Shawn, Zhang, Zhaoduo, Martino, Anthony, and Faulon, Jean-Loup. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. United States: N. p., 2007.
Web. doi:10.1093/bioinformatics/btm021.
Martin, Shawn, Zhang, Zhaoduo, Martino, Anthony, & Faulon, Jean-Loup. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. United States. https://doi.org/10.1093/bioinformatics/btm021
Martin, Shawn, Zhang, Zhaoduo, Martino, Anthony, and Faulon, Jean-Loup. Wed .
"Boolean dynamics of genetic regulatory networks inferred from microarray time series data". United States. https://doi.org/10.1093/bioinformatics/btm021. https://www.osti.gov/servlets/purl/1427005.
@article{osti_1427005,
title = {Boolean dynamics of genetic regulatory networks inferred from microarray time series data},
author = {Martin, Shawn and Zhang, Zhaoduo and Martino, Anthony and Faulon, Jean-Loup},
abstractNote = {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 paper 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. 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. In conclusion, 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.},
doi = {10.1093/bioinformatics/btm021},
journal = {Bioinformatics},
number = 7,
volume = 23,
place = {United States},
year = {Wed Jan 31 00:00:00 EST 2007},
month = {Wed Jan 31 00:00:00 EST 2007}
}
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