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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