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.
Author: | Sven Twardziok, Heike Siebert, Alexander Heyl |
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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 |