Stochasticity in reactions: a probabilistic Boolean modeling approach

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. Categories and Subject Descriptors I.6 [Simulation and Modeling]: Model Development-Modeling methodologies; J [Computer Applications]: Life and Medical Sciences; G [Mathematics of Computing]: Probability and Statistics--Markov processes

• Type: Journal
• Year: 2010
• Where: CMSB
• Authors: Sven Twardziok, Heike Siebert, Alexander Heyl
• Keywords: Applied Computing, Boolean Formalisms, Boolean Modeling Frameworks, CMSB 2010, Important Feedback Mechanism