Literature DB >> 15546934

Application of Petri net theory for modelling and validation of the sucrose breakdown pathway in the potato tuber.

Ina Koch1, Björn H Junker, Monika Heiner.   

Abstract

MOTIVATION: Because of the complexity of metabolic networks and their regulation, formal modelling is a useful method to improve the understanding of these systems. An essential step in network modelling is to validate the network model. Petri net theory provides algorithms and methods, which can be applied directly to metabolic network modelling and analysis in order to validate the model. The metabolism between sucrose and starch in the potato tuber is of great research interest. Even if the metabolism is one of the best studied in sink organs, it is not yet fully understood.
RESULTS: We provide an approach for model validation of metabolic networks using Petri net theory, which we demonstrate for the sucrose breakdown pathway in the potato tuber. We start with hierarchical modelling of the metabolic network as a Petri net and continue with the analysis of qualitative properties of the network. The results characterize the net structure and give insights into the complex net behaviour.

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Year:  2004        PMID: 15546934     DOI: 10.1093/bioinformatics/bti145

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  23 in total

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4.  Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case.

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5.  Time-dependent structural transformation analysis to high-level Petri net model with active state transition diagram.

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7.  Modeling formalisms in Systems Biology.

Authors:  Daniel Machado; Rafael S Costa; Miguel Rocha; Eugénio C Ferreira; Bruce Tidor; Isabel Rocha
Journal:  AMB Express       Date:  2011-12-05       Impact factor: 3.298

8.  Application of Petri net based analysis techniques to signal transduction pathways.

Authors:  Andrea Sackmann; Monika Heiner; Ina Koch
Journal:  BMC Bioinformatics       Date:  2006-11-02       Impact factor: 3.169

9.  A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks.

Authors:  Pedro T Monteiro; Estelle Dumas; Bruno Besson; Radu Mateescu; Michel Page; Ana T Freitas; Hidde de Jong
Journal:  BMC Bioinformatics       Date:  2009-12-30       Impact factor: 3.169

10.  Toward the automated generation of genome-scale metabolic networks in the SEED.

Authors:  Matthew DeJongh; Kevin Formsma; Paul Boillot; John Gould; Matthew Rycenga; Aaron Best
Journal:  BMC Bioinformatics       Date:  2007-04-26       Impact factor: 3.169

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