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Stochasticity in reactions: a probabilistic Boolean modeling approach

Please always quote using this URN:urn:nbn:de:0296-matheon-7042
  • Boolean modeling frameworks have long since proved their worth for capturing and analyzing essential characteristics of complex systems. Hybrid approaches aim at exploiting the advantages of Boolean formalisms while refining expressiveness. In this paper, we present a formalism that augments Boolean models with stochastic aspects. More specifically, biological reactions effecting a system in a given state are associated with probabilities, resulting in dynamical behavior represented as a Markov chain. Using this approach, we model and analyze the cytokinin response network of Arabidopsis thaliana with a focus on clarifying the character of an important feedback mechanism.

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Metadaten
Author:Sven Twardziok, Heike Siebert, Alexander Heyl
URN:urn:nbn:de:0296-matheon-7042
Referee:Alexander Bockmayr
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2010/01/09
Release Date:2010/08/31
Institute:Freie Universität Berlin
Technische Universität Berlin
Zuse Institute Berlin (ZIB)
Preprint Number:713
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