Modelling of biochemical reactions by stochastic automata networks.

*(English)*Zbl 1277.68215
Busi, Nadia (ed.) et al., Proceedings of the first workshop on membrane computing and biologically inspired process calculi (MeCBIC 2006), S. Servolo, Venice, Italy, July 9, 2006. Amsterdam: Elsevier. Electronic Notes in Theoretical Computer Science 171, No. 2, 197-208 (2007).

Summary: This paper presents a stochastic modelling framework based on stochastic automata networks (SANs) for the analysis of complex biochemical reaction networks. Our approach takes into account the discrete character of quantities of components (i.e., the individual populations of the involved chemical species) and the inherent probabilistic nature of microscopic molecular collisions. Moreover, as for process calculi that have recently been applied to systems in biology, the SAN approach has the advantage of a modular design process being adequate for abstraction purposes. The associated composition operator leads to an elegant and compact representation of the underlying continuous-time Markov chain in form of a Kronecker product. SANs have been extensively used in performance analysis of computer systems and a large variety of numerical and simulative analysis algorithms exist. We illustrate that describing a biochemical reaction network by means of a SAN offers promising opportunities to get insight into the quantitative behaviour of systems in biology while taking advantage of the benefits of a compositional modelling approach.

For the entire collection see [Zbl 1273.68017].

For the entire collection see [Zbl 1273.68017].

##### MSC:

68Q85 | Models and methods for concurrent and distributed computing (process algebras, bisimulation, transition nets, etc.) |

68Q45 | Formal languages and automata |

68Q87 | Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) |

92C40 | Biochemistry, molecular biology |

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\textit{V. Wolf}, Electron. Notes Theor. Comput. Sci. 171, No. 2, 197--208 (2007; Zbl 1277.68215)

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