This book constitutes the thoroughly refereed post-workshop proceedings of the Second International Workshop on Hybrid Systems Biology, HSB 2013, held as part of the ECAL 2013 event, in Taormina, Italy, in September 2013; and the Third International Workshop on Hybrid Systems Biology, HSB 2014, held as part of CAV 2014, in Vienna, Austria, in July 2014. This volume presents 8 full papers together with 2 invited tutorials/surveys from 21 submissions. The HSB 2013 workshop aims at collecting scientists working in the area of hybrid modeling applied to systems biology, in order to discuss about current achieved goals, current challenges and future possible developments. The scope of the HSB 2014 workshop is the general area of dynamical models in biology with an emphasis on hybrid approaches, which are not restricted to a narrow class of mathematical models, and which take advantage of techniques developed separately in different sub-fields. “br> /div
This book constitutes the refereed proceedings of the 5th International Workshop on Hybrid Systems Biology, HSB 2016, held in Grenoble, France, in October 2016. The 11 full papers presented in this book were carefully reviewed and selected from 26 submissions. They were organized and presented in 4 thematic sessions also reflected in this book: model simulation; model analysis; discrete and network modelling; stochastic modelling for biological systems.
This book constitutes the thoroughly referred post-workshop proceedings of the 4th International Workshop on Hybrid Systems biology, HSB 2015, held as part of the Madrid Meet 2015 event, in Madrid, Spain in September 2015. The volume presents 13 full papers together with 2 abstracts of invited sessions from 18 submissions. The scope of the HSB workshop is the general area of dynamical models in Biology with an emphasis on hybrid approaches — by no means restricted to a narrow class of mathematical models — and taking advantage of techniques developed separately in different areas.
This book constitutes the thoroughly refereed proceedings of the 6th International Workshop on Hybrid Systems Biology, HSB 2019, held in Prague, Czech Republic, in April 2019. The 8 full papers presented in this book together with 1 short paper and 3 invited papers were carefully reviewed and selected from 13 submissions. They cover topics such as: modeling and analysis of metabolic, signaling, and genetic regulatory networks in living cells; models of tissues, organs, physiological models; models and methods coping with incomplete, uncertain and heterogeneous information including learning for biological systems, parametric synthesis and inference; stochastic and hybrid models in biology; hierarchical systems for multi-scale, multi-domain analysis; abstraction, approximation, discretization, and model reduction techniques; modeling, analysis and design for synthetic biology, cyber-biological systems and biomedical studies (e.g. therapies, teleoperation); game-theoretical frameworks and population models in biology (e.g. mixed-effects and Bayesian modeling); biological applications of quantitative and formal analysis techniques (e.g. reachability computation, model checking, abstract interpretation, bifurcation theory, stability and sensitivity analysis); efficient techniques for combined and heterogeneous (stochastic/deterministic, spatial/non-spatial) simulations for biological models; modeling languages and logics for biological systems with related analysis and simulation tools; and control architectures of biological systems including biology-in-the-loop systems and bio-robotics.
This volume, part of the Advances in Molecular Biology series, presents work by pioneers in the field and is the first publication devoted solely to the yeast two-hybrid system. It includes detailed protocols, practical advice on troubleshooting, and suggestions for future development. In addition, it illustrates how to construct an activation domain hybrid library, how to identify mutations that disrupt an interaction, and how to use the system in mammalian cells. Many of the contributors have developed new applications and variations of the technique.
This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies. This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.
The emerging, multi-disciplinary field of systems biology is devoted to the study of the relationships between various parts of a biological system, and computer modeling plays a vital role in the drive to understand the processes of life from an holistic viewpoint. Advancements in experimental technologies in biology and medicine have generated an enormous amount of biological data on the dependencies and interactions of many different molecular cell processes, fueling the development of numerous computational methods for exploring this data. The mathematical formalism of Petri net theory is able to encompass many of these techniques. This essential text/reference presents a comprehensive overview of cutting-edge research in applications of Petri nets in systems biology, with contributions from an international selection of experts. Those unfamiliar with the field are also provided with a general introduction to systems biology, the foundations of biochemistry, and the basics of Petri net theory. Further chapters address Petri net modeling techniques for building and analyzing biological models, as well as network prediction approaches, before reviewing the applications to networks of different biological classification. Topics and features: investigates the modular, qualitative modeling of regulatory networks using Petri nets, and examines an Hybrid Functional Petri net simulation case study; contains a glossary of the concepts and notation used in the book, in addition to exercises at the end of each chapter; covers the topological analysis of metabolic and regulatory networks, the analysis of models of signaling networks, and the prediction of network structure; provides a biological case study on the conversion of logical networks into Petri nets; discusses discrete modeling, stochastic modeling, fuzzy modeling, dynamic pathway modeling, genetic regulatory network modeling, and quantitative analysis techniques; includes a Foreword by Professor Jens Reich, Professor of Bioinformatics at Humboldt University and Max Delbrück Center for Molecular Medicine in Berlin. This unique guide to the modeling of biochemical systems using Petri net concepts will be of real utility to researchers and students of computational biology, systems biology, bioinformatics, computer science, and biochemistry.
Paul N. MacDonald has assembled a collection of powerful molecular tools for examining and characterizing protein-protein, protein-DNA, and protein-RNA interactions. The techniques range from the most basic (introducing plasmids into yeasts, interaction assays, and recovering the plasmids from yeast), to the most advanced alternative strategies (involving one-hybrid, split two-hybrid, three-hybrid, membrane recruitment systems, and mammalian systems). Methods are also provided for dealing with the well-known problem of artifacts and false positives and for identifying the interacting partners in important biological systems, including the SMAD and nuclear receptor pathways. To ensure ready reproducibility and robust results, each technique is described in step-by-step detail by researchers who employ it regularly.
Because they incorporate both time- and event-driven dynamics, stochastic hybrid systems (SHS) have become ubiquitous in a variety of fields, from mathematical finance to biological processes to communication networks to engineering. Comprehensively integrating numerous cutting-edge studies, Stochastic Hybrid Systems presents a captivating treatment of some of the most ambitious types of dynamic systems. Cohesively edited by leading experts in the field, the book introduces the theoretical basics, computational methods, and applications of SHS. It first discusses the underlying principles behind SHS and the main design limitations of SHS. Building on these fundamentals, the authoritative contributors present methods for computer calculations that apply SHS analysis and synthesis techniques in practice. The book concludes with examples of systems encountered in a wide range of application areas, including molecular biology, communication networks, and air traffic management. It also explains how to resolve practical problems associated with these systems. Stochastic Hybrid Systems achieves an ideal balance between a theoretical treatment of SHS and practical considerations. The book skillfully explores the interaction of physical processes with computerized equipment in an uncertain environment, enabling a better understanding of sophisticated as well as everyday devices and processes.
This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. - Logical information flow aids understanding of basic building blocks of life through disease phenotypes - Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns - Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation - Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.
