Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach
Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach
- Author(s): N. Yalamanchili ; D.E. Zak ; B.A. Ogunnaike ; J.S. Schwaber ; A. Kriete ; B.N. Kholodenko
- DOI: 10.1049/ip-syb:20050090
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- Author(s): N. Yalamanchili 1 ; D.E. Zak 2, 3 ; B.A. Ogunnaike 3 ; J.S. Schwaber 2 ; A. Kriete 1 ; B.N. Kholodenko 2
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View affiliations
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Affiliations:
1: School of Biomedical Engineering and Health Sciences, Drexel University, Philadelphia, USA
2: Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics/Computational Biology, Thomas Jefferson University, Philadelphia, USA
3: Department of Chemical Engineering, University of Delaware, Newark, USA
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Affiliations:
1: School of Biomedical Engineering and Health Sciences, Drexel University, Philadelphia, USA
- Source:
Volume 153, Issue 4,
July 2006,
p.
236 – 246
DOI: 10.1049/ip-syb:20050090 , Print ISSN 1741-2471, Online ISSN 1741-248X
Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.
Inspec keywords: biochemistry; biology computing; genetics; molecular biophysics
Other keywords:
Subjects: Biology and medical computing; Model reactions in molecular biophysics; General, theoretical, and mathematical biophysics
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