Werner Sandmann and Verena Wolf
A Computational Stochastic Modeling Formalism for Biological Networks
498 - 503
2008
2
2
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/5322
https://publications.waset.org/vol/14
World Academy of Science, Engineering and Technology
Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the socalled chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storageefficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a &quot;higherlevel paradigm&quot; with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the models state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than nonsimulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply nonsimulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzymecatalyzed substrate conversion and a part of the bacteriophage &lambda; lysislysogeny pathway.
Open Science Index 14, 2008