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Collective Behavior In Systems Biology: A Primer on Modeling Infrastructure offers a survey of established and emerging methods for quantifying process behavior in cellular systems. It introduces and applies mathematics and related abstract methods to processes in biological systems - why they are used, how they work, and what they mean. Emphasizing differential equations in an interdisciplinary approach, this book discusses infrastructure for kinetic modeling, technological system and control theories, optimization, and process behavior in cellular networks. The knowledge that the reader gains will be valuable for entering and keeping up with a rapidly developing discipline. - Introduces basics of mathematical and abstract methods for understanding, predicting, and modifying collective behavior in cellular systems - Targets biomedical professionals as well as computational specialists who are willing to take advantage of novel high-throughput data acquisition technologies
Using examples from finance and modern warfare to the flocking of birds and the swarming of bacteria, the collected research in this volume demonstrates the common methodological approaches and tools for modeling and simulating collective behavior. The topics presented point toward new and challenging frontiers of applied mathematics, making the volume a useful reference text for applied mathematicians, physicists, biologists, and economists involved in the modeling of socio-economic systems.
This book constitutes the refereed proceedings of the 18th International Conference on Computational Methods in Systems Biology, CMSB 2020, held in Konstanz, Germany, in September 2020.* The 17 full papers and 5 tool papers were carefully reviewed and selected from 30 submissions. In addition 3 abstracts of invited talks and 2 tutorials have been included in this volume. 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. * The conference was held virtually due to the COVID-19 pandemic.
A groundbreaking new perspective on collective behavior across biological systems Collective behavior is everywhere in nature, from gene transcription and cancer cells to ant colonies and human societies. It operates without central control, using local interactions among participants to allow groups to adjust to changing conditions. The Ecology of Collective Behavior brings together ideas from evolutionary biology, network science, and dynamical systems to present an ecological approach to understanding how the interactions of individuals generate collective outcomes. Deborah Gordon argues that the starting point for explaining how collective behavior works in any natural system is to consider how it changes in relation to the changing world around it. She shows how feedback use—the means by which networks of interactions operate—and the organization of interaction networks evolve to reflect the stability and demands of the environment. Ant colonies function collectively, and the enormous diversity of species in different habitats provides opportunities to look for general ecological patterns. Through an in-depth comparison of ant species, Gordon identifies broad trends in how the diversity of collective behavior in many other collective systems reflects the dynamics of the environment. Shedding light on how individual actions give rise to group behavior, The Ecology of Collective Behavior explains the evolution of collective behavior through innovation in participant interactions, offering new insights into how collective responses function in changing conditions.
Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.
This book presents the theoretical foundations of Systems Biology, as well as its application in studies on human hosts, pathogens and associated diseases. This book presents several chapters written by renowned experts in the field. Some topics discussed in depth in this book include: computational modeling of multiresistant bacteria, systems biology of cancer, systems immunology, networks in systems biology.
Systems Biology and In-Depth Applications for Unlocking Diseases: Principles, tools and Application to Disease provides the essence of systems biology approaches in a practical manner, illustrating the basic principles essential to develop and model in real life science applications. Methodologies covered show how to interrogate biological data, with the purpose of obtaining insight about disease diagnosis, prognosis, and treatment. Sections provide an introduction and history of systems biology, discuss the tools and resources needed for structure and function of biological systems, and present evidence of systems biology in action. Examples include big data techniques, scale networks, mathematical model development, and much more. This is the perfect reference to provide the fundamental base of knowledge needed for systems biologists, professionals in systems medicine, computational biologists, and bioinformaticians. - Provides detailed and comprehensive coverage of the field of systems biology - Delivers instruction on how to interrogate biological data, with the purpose of obtaining insight about disease diagnosis, prognosis, and treatment - Makes effective steps towards personalized medicine in the treatment of disease - Explains effective disease treatment strategies at early diagnosis stages
"This book highlights the use of systems approaches including genomic, cellular, proteomic, metabolomic, bioinformatics, molecular, and biochemical, to address fundamental questions in complex diseases like cancer diabetes but also in ageing"--Provided by publisher.