Stochastic Modelling For Systems Biology
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Stochastic Modelling for Systems Biology
Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective. Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications. While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.
Stochastic Modelling for Systems Biology
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. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessib.
Stochastic Models in Biology
This monograph, first published in 1974, is an attempt to demonstrate the usefulness of the theory of stochastic processes in understanding biologic phenomena at various levels of complexity - from the molecular to the ecologic level. The modeling of biologic systems via stochastic processes allows the incorporation of effects of secondary factors for which a detailed knowledge is missing. In the first two chapters of the monograph, the authors present the mathematic analysis used in the later chapters. The authors attempted to make the chapters self-contained and to make the book comprehensive, bringing in results derived by different authors using a variety of techniques and notations. In later chapters, where models of various biologic phenomena are discussed, introductory reviews of those phenomena are given for readers with less biologic background. Nira Dyn is a professor of Applied Mathematics at Tel-Aviv University in Israel since 1984. Her main field of activity now is Geometric Modeling. Her previous research interests were in Approximation Theory and in Mathematical Modeling of Biological systems. She is now serving in the editorial boards of the Journal of Approximation Theory and of the journal Computer Aided Geometric Design. She wrote more than 130 papers, and participated actively in more than 80 conferences and workshops. Her education was in Applied Mathematics in Israeli institutions; B.Sc from the Technion, Haifa, in 1965, M.Sc from the Weizmann Institute, Rehovot, in 1967, Ph.D. from the Weizmann Institute, Rehovot in 1970.