skip to main content

DOE PAGESDOE PAGES

Title: Boolean dynamics of genetic regulatory networks inferred from microarray time series data

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:
 [1] ;  [2] ;  [3] ;  [4]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biology Dept.
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biosystems Research
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Biomolecular Analysis and Imaging
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biosciences Dept.
Publication Date:
Report Number(s):
SAND2007-0437J
Journal ID: ISSN 1367-4803; 524009
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Volume: 23; Journal Issue: 7; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
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
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION
OSTI Identifier:
1427005

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., 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. doi:10.1093/bioinformatics/btm021.
Martin, Shawn, Zhang, Zhaoduo, Martino, Anthony, and Faulon, Jean-Loup. 2007. "Boolean dynamics of genetic regulatory networks inferred from microarray time series data". United States. doi: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 = {2007},
month = {1}
}