Recently, stochastic process algebras have been used for modeling biological systems, as they provide a simple, compositional language to define biological models. Classical process algebras, however, require to express everything in terms of communications, a limiting restriction when structured information needs to be processed. In this book, we tackle this problem using a more general language and programming it to deal with the domain of interest. Specifically, we introduce a stochastic extension (sCCP) of Concurrent Constraint Programming. It is precisely the use of constraints that gives flexibility and extendibility to the language, together with the presence of context-dependent stochastic rates. To prove this, we show how sCCP can be used to model a wide range of biological systems, from biochemical networks to the process of folding of a protein, providing evidence that the use of constraints simplifies the process of modeling and allows to incorporate external knowledge. Many analysis tools are developed: stochastic simulation, model checking, ODE-based approximations. The book can be of interest to researchers working in the field of computational systems biology.
Recently, stochastic process algebras have been used for modeling biological systems, as they provide a simple, compositional language to define biological models. Classical process algebras, however, require to express everything in terms of communications, a limiting restriction when structured information needs to be processed. In this book, we tackle this problem using a more general language and programming it to deal with the domain of interest. Specifically, we introduce a stochastic extension (sCCP) of Concurrent Constraint Programming. It is precisely the use of constraints that gives flexibility and extendibility to the language, together with the presence of context-dependent stochastic rates. To prove this, we show how sCCP can be used to model a wide range of biological systems, from biochemical networks to the process of folding of a protein, providing evidence that the use of constraints simplifies the process of modeling and allows to incorporate external knowledge. Many analysis tools are developed: stochastic simulation, model checking, ODE-based approximations. The book can be of interest to researchers working in the field of computational systems biology.
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