Stochasticity in reactions: a probabilistic Boolean modeling approach

Authors:
Sven Twardziok;Heike Siebert;Alexander Heyl
Affiliations:
Charité Berlin, Institute for Microbiology and Bioinformatics, Berlin, Germany;Freie Universität Berlin, Berlin, Germany;Freie Universität Berlin, Berlin, Germany
Venue:
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
Year:
2010

Citing 2
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Abstract

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.