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‘Stochastic Modelling for Systems Biology’ was designed to fill an important gap in the educational materials available for students learning about modelling methods for biological systems. Specifically, while stochastic models are emerging as perhaps the preferred method for modelling cellular and subcellular biochemistry in research practice, they remain unfamiliar to most of those who are not specialists in the field. The underlying mathematical and computational methods are well described in the literature of other fields, but the translation to biological practice is largely documented only in the current scientific literature. There are few teaching materials available for these models, particularly for beginning students in biological modelling who lack the background to follow the current scientific literature or the dense mathematical treatments available in texts from other fields. The material in this book arose out of a class the author teaches on stochastic systems biology to master’s students in bioinformatics. The text therefore takes a practice-oriented approach to the material, assuming a limited background, focusing on practical considerations in model design and implementation, and making extensive use of example systems. The text provides a solid overview of the basics of stochastic kinetic modelling for the model developer. Chapter 1 introduces the topic by covering some basic concepts and applications of modelling for biology. Chapter 2 describes some representations of biochemical models that are used throughout the rest of the text. Chapters 3 through 5 then provide background material helpful in following the later sections. Chapter 3 covers some basic probability theory and a few important probability distributions, Chapter 4 techniques for sampling from probability distributions in general, and Chapter 5 concepts in Markov models including extensions to continuous time and space. Chapters 6 and 8 provide the bulk of the material specific to stochastic models in biology. Chapter 6 covers the basic theory, models, and methods behind standard Gillespie simulations of reaction chemistry. Chapter 8 provides a more thorough consideration of algorithmic issues in stochastic chemical modelling, including a survey of the leading methods for exact and approximate stochastic kinetic models. In between, Chapter 7 provides four extended examples: dimerization systems, Michaelis–Menten enzyme kinetics, a generic autoregulatory gene network, and the lac operon. Chapters 9 and 10 discuss techniques for model fitting, providing a useful if not exhaustive overview of some common methods. This is indeed a timely addition to the literature and nicely fills the gap Wilkinson identifies in the available teaching materials for biological modelling. The practical, hands-on approach Wilkinson takes will not satisfy all readers. Treatment of background theory is sparse and even non-specialists may find that they need to delve into the suggestions for further reading. But this approach makes the book more useful and accessible to the beginner than a denser but deeper text would be. The book is filled with useful practical advice on the gaps between theoretical models and realistic systems and data sets, as well as techniques for bridging these gaps in practice. There are many pointers to other texts and primary literature that should meet the needs of those requiring greater theoretical depth than this text provides. The text also has a companion website on which the author intends to keep an up-to-date directory of literature and tools for systems biology modelling. Wilkinson’s practice-oriented approach is also reflected in the several extended examples presented in the text, which are likely to greatly help the beginner. Formal specifications for these examples are provided in the Systems Biology Markup Language (SBML), which should make it BRIEFINGS IN BIOINFORMATICS. VOL 8. NO 3. 204^205 doi:10.1093/bib/bbm001 Advance Access publication February 17, 2007

Stochastic Modelling for Systems Biology. * Darren J. Wilkinson