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Understanding Network Behavior by Structured Representations of Transition Invariants

  • Monika HeinerEmail author
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

Petri nets offer a bipartite and concurrent paradigm, and consequently represent a natural choice for modeling and analyzing biochemical networks. We introduce a Petri net structuring technique contributing to a better understanding of the network behavior and requiring static analysis only. We determine a classification of the transitions into abstract dependent transition sets, which induce connected subnets overlapping in interface places only. This classification allows a structured representation of the transition invariants by network coarsening. The whole approach is algorithmically defined, and thus does not involve human interaction. This structuring technique is especially helpful for analyzing biochemically interpreted Petri nets, where it supports model validation of biochemical reaction systems reflecting current comprehension and assumptions of what has been designed by natural evolution.

Keywords

Synthetic Biology Incidence Matrix Biochemical Network System Biology Markup Language Signal Transduction Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Computer ScienceBrandenburg University of TechnologyCottbusGermany

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