This book constitutes the refereed proceedings of the 17th International Conference on Computational Methods in Systems Biology, CMSB 2019, held in Trieste, Italy, in September 2019. The 14 full papers, 7 tool papers and 11 posters were carefully reviewed and selected from 53 submissions. Topics of interest include formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, analysis, and simulation of biological systems; high-performance computational systems biology and parallel implementations; model inference from experimental data; model integration from biological databases; multi-scale modeling and analysis methods; computational approaches for synthetic biology; and case studies in systems and synthetic biology.

This book constitutes the proceedings of the 14th International Conference on Quantitative Evaluation Systems, QEST 2017, held in Berlin, Germany, in September 2017.The 20 full papers and 4 tool papers presented were carefully reviewed and selected From 58 submissions. The papers are organized in topical sections entitled: probabilistic modeling; smart energy systems over the cloud; Petri nets and performance modeling; parametric verification; machine learning and formal methods; tools.

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.