Petri nets as a framework for the reconstruction and analysis of signal transduction pathways and regulatory networks

Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we exemplify how molecular mechanisms, biochemical or gene...

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Bibliographic Details
Published in:Natural computing 2011-06, Vol.10 (2), p.639-654
Main Authors: Marwan, Wolfgang, Wagler, Annegret, Weismantel, Robert
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Cellular
Complex Systems
Computer Science
Evolutionary Biology
Genetics
Mathematical models
Molecular biology
Networks
Pathways
Petri nets
Processor Architectures
Reconstruction
Signal transduction
Theory of Computation
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Summary:Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we exemplify how molecular mechanisms, biochemical or genetic, can be consistently respresented in the form of place/transition Petri nets. We then describe the application of Petri nets to the reconstruction of molecular and genetic networks from experimental data and their power to represent biological processes with arbitrary degree of resolution of the subprocesses at the cellular and the molecular level. Petri nets are executable formal language models that permit the unambiguous visualization of regulatory mechanisms, and they can be used to encode the results of mathematical algorithms for the reconstruction of causal interaction networks from experimental time series data.
ISSN:1567-7818
1572-9796
DOI:10.1007/s11047-009-9152-x