Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach.
BACKGROUND
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.


BACKGROUND
The Petri net model construction tool and the data files for the B. subtilis sporulation case study are available at http://bioinf.ncl.ac.uk/gnapn.
Steggles
L Jason
LJ
School of Computing Science, University of Newcastle, Newcastle upon Tyne, UK. L.J.Steggles@ncl.ac.uk
Banks
Richard
R
Shaw
Oliver
O
Wipat
Anil
A
D000465:Algorithms; D002468:Cell Physiological Phenomena; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016015:Logistic Models; D008954:Models, Biological; D016571:Neural Networks, Computer; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome