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Qualitatively modelling and analysing genetic regulatory networks: A Petri net approach

Lookup NU author(s): Dr Jason Steggles, Dr Oliver Shaw, Professor Anil Wipat

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Abstract

Motivation: New developments in post-genomic technology now provide researchers with the data necessary to study regulatory processes in a holistic fashion at multiple levels of biological organization. One of the major challenges for the biologist is to integrate and interpret these vast data resources to gain a greater understanding of the structure and function of the molecular processes that mediate adaptive and cell cycle driven changes in gene expression. In order to achieve this biologists require new tools and techniques to allow pathway related data to be modelled and analysed as network structures, providing valuable insights which can then be validated and investigated in the laboratory. Results: We propose a new technique for constructing and analysing qualitative models of genetic regulatory networks based on the Petri net formalism. We take as our starting point the Boolean network approach of treating genes as binary switches and develop a new Petri net model which uses logic minimization to automate the construction of compact qualitative models. Our approach addresses the shortcomings of Boolean networks by providing access to the wide range of existing Petri net analysis techniques and by using non-determinism to cope with incomplete and inconsistent data. The ideas we present are illustrated by a case study in which the genetic regulatory network controlling sporulation in the bacterium Bacillus subtilis is modelled and analysed. © 2007 Oxford University Press.


Publication metadata

Author(s): Steggles LJ, Banks R, Shaw O, Wipat A

Publication type: Article

Publication status: Published

Journal: Bioinformatics

Year: 2007

Volume: 23

Issue: 3

Pages: 336-343

ISSN (print): 1367-4803

ISSN (electronic): 1367-4811

Publisher: Oxford University Press

URL: http://dx.doi.org/10.1093/bioinformatics/btl596

DOI: 10.1093/bioinformatics/btl596

PubMed id: 17121774


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