Natural Computing

, Volume 10, Issue 3, pp 1077–1097

Petri net models for the semi-automatic construction of large scale biological networks

Authors

  • Ming Chen
    • Bioinformatics Department, College of Life SciencesZhejiang University
  • Sridhar Hariharaputran
    • Bioinformatics Department, Faculty of TechnologyBielefeld University
    • Bioinformatics Department, Faculty of TechnologyBielefeld University
  • Benjamin Kormeier
    • Bioinformatics Department, Faculty of TechnologyBielefeld University
  • Sarah Spangardt
    • Bioinformatics Department, Faculty of TechnologyBielefeld University
Article

DOI: 10.1007/s11047-009-9151-y

Cite this article as:
Chen, M., Hariharaputran, S., Hofestädt, R. et al. Nat Comput (2011) 10: 1077. doi:10.1007/s11047-009-9151-y

Abstract

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. During the last 15 years, Petri nets have attracted more and more attention to help to solve this key problem. Regarding the published papers, it seems clear that hybrid functional Petri nets are the adequate method to model complex biological networks. Today, a Petri net model of biological networks is built manually by drawing places, transitions and arcs with mouse events. Therefore, based on relevant molecular database and information systems biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the application of Petri nets for modeling and simulation of biological networks. Furthermore, we will present a type of access to relevant metabolic databases such as KEGG, BRENDA, etc. Based on this integration process, the system supports semi-automatic generation of the correlated hybrid Petri net model. A case study of the cardio-disease related gene-regulated biological network is also presented. MoVisPP is available at http://agbi.techfak.uni-bielefeld.de/movispp/.

Keywords

Functional Petri net Model construction Data integration Cardio-disease network MoVisPP

Copyright information

© Springer Science+Business Media B.V. 2009