This book highlights the theory and practical applications of the chemical master equation (CME) approach for very large biochemical networks, which provides a powerful general framework for model building in a variety of biological networks. The aim of the book is to not only highlight advanced numerical solution methods for the CME, but also reveal their potential by means of practical examples. The case studies presented are mainly from biology; however, the applications from novel methods are discussed comprehensively, underlining the interdisciplinary approach in simulation and the potential of the chemical master equation approach for modelling bionetworks. The book is a valuable guide for researchers, graduate students, and professionals alike.

This monograph presents the development of novel model-based methodologies for engineering self-organized and self-assembled systems. The work bridges the gap between statistical mechanics and control theory by tackling a number of challenges for a class of distributed systems involving a specific type of constitutive components, namely referred to as Smart Minimal Particles. The results described in the volume are expected to lead to more robust, dependable, and inexpensive distributed systems such as those endowed with complex and advanced sensing, actuation, computation, and communication capabilities.

Thisvolumecontainstheproceedingsofthe?rstinternationalmeetingonFormal Methods in Systems Biology, held at Microsoft Research, Cambridge, UK, June 4–5, 2008. While there are several venues that cover computational methods in systems biology,there is to date no single conference that brings together the application of the range of formal methods in biology. Therefore, convening such a meeting could prove extremely productive. The purpose of this meeting was to identify techniques for the speci?cation, development and veri?cation of biological m- els.Italsofocusedonthedesignoftoolstoexecuteandanalyzebiologicalmodels in ways that can signi?cantly advance our understanding of biological systems. As a forum for this discussion we invited key scientists in the area of formal methods to this unique meeting. Although this was a one-o? meeting, we are exploring the possibility of this forming the ?rst of what might become an annual conference. Presentations at the meeting were by invitation only; future meetings are expected to operate on a submission and review basis. The Steering Committee and additional referees reviewed the invited papers. Each submission was evaluated by at least two referees. The volume includes nine invited contributions. Formal Methods in Systems Biology 2008 was made possible by the cont- bution and dedication ofmany people. First of all,we wouldlike to thank allthe authors who submitted papers. Secondly, we would like to thank our additional invited speakers and participants. We would also like to thank the members of the Steering Committee for their valuable comments. Finally, we ackno- edge the help of the administrative and technical sta? at the MicrosoftResearch Cambridge lab.

This textbook focuses on stochastic analysis in systems biology containing both the theory and application. While the authors provide a review of probability and random variables, subsequent notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of stochastic framework for modeling subcellular biochemical systems. In particular, the authors make an effort to show how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property. The text contains many illustrations, examples and exercises to illustrate the ideas and methods that are introduced. Matlab code is also provided where appropriate. Additionally, the cell cycle is introduced as a more complex case study. Senior undergraduate and graduate students in mathematics and physics as well as researchers working in the area of systems biology, bioinformatics and related areas will find this text useful.

While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.

By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms Sampling techniques for estimating evolutionary rates and generating molecular structures Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations End-of-chapter exercises

Chemical Biophysics provides an engineering-based approach to biochemical system analysis for graduate-level courses on systems biology, computational bioengineering and molecular biophysics. It is the first textbook to apply rigorous physical chemistry principles to mathematical and computational modeling of biochemical systems for an interdisciplinary audience. The book is structured to show the student the basic biophysical concepts before applying this theory to computational modeling and analysis, building up to advanced topics and research. Topics explored include the kinetics of nonequilibrium open biological systems, enzyme mediated reactions, metabolic networks, biological transport processes, large-scale biochemical networks and stochastic processes in biochemical systems. End-of-chapter exercises range from confidence-building calculations to computational simulation projects.

Quantum physics provides the concepts and their mathematical formalization that lend themselves to describe important properties of biological networks topology, such as vulnerability to external stress and their dynamic response to changing physiological conditions. A theory of networks enhanced with mathematical concepts and tools of quantum physics opens a new area of biological physics, the one of systems biological physics.

Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science and computer science (just to name a few) and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions. We are now in an era of 'big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at nation-wide level thus giving us another source of highly related (causal) 'big data'. This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key view point leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing'. A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks.