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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach
Systems biology, v 153(4), pp 236-246
01 Jul 2006
PMID: 16986625
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Large, complex datasets 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 analyzing
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, we are able to evaluate the accuracy of the modular approach and its sensitivity to key assumptions.
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Details
- Title
- Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach
- Creators
- Nirupama Yalamanchili - Drexel UniversityDaniel E. Zak - Thomas Jefferson UniversityBabatunde A. Ogunnaike - University of DelawareJames S. Schwaber - Thomas Jefferson UniversityAndres Kriete - Drexel UniversityBoris N. Kholodenko - Thomas Jefferson University
- Publication Details
- Systems biology, v 153(4), pp 236-246
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000239773700012
- Scopus ID
- 2-s2.0-33746585383
- Other Identifier
- 991019168820704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Cell Biology
- Mathematical & Computational Biology