Computational modeling is emerging as a powerful new approach to study and manipulate biological systems. Multiple methods have been developed to model, visualize, and rationally alter systems at various length scales, starting from molecular modeling and design at atomic resolution to cellular pathways modeling and analysis. Higher time and length scale processes, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. This book provides an overview of the established computational methods used for modeling biologically and medically relevant systems.

This unique text explores the use of innovative modeling techniques in effecting a better understanding of complex diseases such as AIDS and cancer. From a way of representing the computational properties of protein-folding problems to computer simulation of bimodal neurons and networks, Computer Modeling and Simulations of Complex Biological Systems examines several modeling methodologies and integrates them across a variety of disciplines. This interdisciplinary approach suggests new ways to solve complex problems pertaining to biological systems. Written in clear and simple terms appropriate for both the novice and the experienced researcher, the book presents a step-by-step approach to the subject and includes numerous examples that explain the concepts presented in the text.

:Computer modeling of biological systems, a process of building new sets of conceptualizations, theories, and implementations is cov-ered in depth. The emphasis is on how computer modeling encom-passes three distinct areas of science (computer science, biology, and medicine) for building a framework for investigating the behavior of a complex biological system. Simulation and modeling are dis-cussed because these two factors provide the methodology for the design, development, experimentation, analysis, and evaluation of a experiment under study. Because of their high degree of modular-ity and collective complexity, computer systems such as analog, digital, or hybrid are reviewed. This presentation of computer mod-eling of biological systems delves into the study of data represen-tation (or data structures) and their transformation into an empirical model via a software program.

Systems Biology is the systematic study of the interactions between the components of a biological system and studies how these interactions give rise to the function and behavior of the living system. Through this, a life process is to be understood as a whole system rather than the collection of the parts considered separately. Systems Biology is therefore more than just an emerging field: it represents a new way of thinking about biology with a dramatic impact on the way that research is performed. The logical approach provides an intuitive method to provide explanations based on an expressive relational language. This book covers various aspects of logical modeling of biological systems, bringing together 10 recent logic-based approaches to Systems Biology by leading scientists. The chapters cover the biological fields of gene regulatory networks, signaling networks, metabolic pathways, molecular interaction and network dynamics, and show logical methods for these domains based on propositional and first-order logic, logic programming, answer set programming, temporal logic, Boolean networks, Petri nets, process hitting, and abductive and inductive logic programming. It provides an excellent guide for all scientists, biologists, bioinformaticians, and engineers, who are interested in logic-based modeling of biological systems, and the authors hope that new scientists will be encouraged to join this exciting scientific endeavor.

This book delivers a comprehensive and insightful account of applying mathematical modelling approaches to very large biological systems and networks—a fundamental aspect of computational systems biology. The book covers key modelling paradigms in detail, while at the same time retaining a simplicity that will appeal to those from less quantitative fields. Key Features: A hands-on approach to modelling Covers a broad spectrum of modelling, from static networks to dynamic models and constraint-based models Thoughtful exercises to test and enable understanding of concepts State-of-the-art chapters on exciting new developments, like community modelling and biological circuit design Emphasis on coding and software tools for systems biology Companion website featuring lecture videos, figure slides, codes, supplementary exercises, further reading, and appendices: https://ramanlab.github.io/SysBioBook/ An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks is highly multi-disciplinary and will appeal to biologists, engineers, computer scientists, mathematicians and others.

This book constitutes the refereed proceedings of the 13th International Conference on Computational Methods in Systems Biology, CMSB 2015, held in Nantes, France, in September 2015. The 20 full papers and 2 short papers presented were carefully reviewed and selected from 43 full and 4 short paper submissions. The papers cover a wide range of topics in the analysis of biological systems, networks and data such as model checking, stochastic analysis, hybrid systems, circadian clock, time series data, logic programming, and constraints solving ranging from intercellular to multiscale.

Rovereto,December2002 CorradoPriami ProgrammeCommitteeofCMSB 2003 CorradoPriami(Chair),UniversityofTrento(Italy), CharlesAu?ray,CNRS,Villejuif(France), CosimaBaldari,Universit`adiSiena(Italy), AlexanderBockmayr,Universit ́eHenriPoincar ́e(France), LucaCardelli,MicrosoftResearchCambridge(UK), VincentDanos,Universit ́eParisVII(France), PierpaoloDegano,Universitad ` iPisa(Italy), Francois ̧ Fages,INRIA,Rocquencourt(France), DrabløsFinn,NorwegianUniversityofScienceandTechnology,Trondheim(N- way), MonikaHeiner,BrandenburgUniversityofTechnologyatCottbus(Germany), InaKoch,UniversityofAppliedSciencesBerlin,(Germany), JohnE.

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists. - Provides a unified presentation of network inference, analysis, and modeling - Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systems - Includes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology - Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections: • Reviews of Computational Methods • Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels • The Interface of Biotic and Abiotic Processes • Processing of Large Data Sets for Enhanced Analysis • Parameter Optimization and Measurement