Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
