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Petri Nets for Modeling and Analyzing Biochemical Reaction Networks

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Approaches in Integrative Bioinformatics

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Abstract

Petri nets have been widely used to model and analyze biochemical reaction networks. This chapter gives an overview of different types of Petri nets within a unifying Petri net framework that comprises the qualitative, stochastic, continuous, and hybrid paradigms at both uncolored and colored levels. The Petri net framework permits to investigate one and the same biological reaction network with different modeling abstractions in various complementary ways. We describe the use of the framework to investigate biochemical reaction networks with the help of the unifying Petri net tool, Snoopy, and its close friends Charlie and Marcie. The repressilator example serves as running case study.

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Acknowledgements

This work has been supported by Germany Federal Ministry of Education and Research (0315449H), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.2009005), and National Natural Science Foundation of China (61273226). We would like to thank David Gilbert and Wolfgang Marwan for many fruitful discussions and Mary Ann Blätke, Mostafa Herajy, Christian Rohr, and Martin Schwarick for their assistance in model construction, software development, and model checking.

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Authors and Affiliations

  1. Control and Simulation Center, Harbin Institute of Technology, 3006, 150080, Harbin, China

    Fei Liu

  2. Department of Computer Science, Brandenburg University of Technology, 10 13 44, 03013, Cottbus, Germany

    Monika Heiner

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  1. Fei Liu
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Correspondence to Fei Liu .

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Editors and Affiliations

  1. College of Life Sciences, Zhejiang University, Hangzhou, People's Republic of China

    Ming Chen

  2. Department of Bioinformatics and Medical Informatics, Bielefeld University, Bielefeld, Germany

    Ralf Hofestädt

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Liu, F., Heiner, M. (2014). Petri Nets for Modeling and Analyzing Biochemical Reaction Networks. In: Chen, M., Hofestädt, R. (eds) Approaches in Integrative Bioinformatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41281-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-41281-3_9

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