Author(s): Christopher P. Fall, Eric S. Marland, John M. Wagner and John J. Tyson, Editors
Springer Verlag, 1st edition (July 9, 2002)
Hardcover: 488 pages
Computational Cell Biology is a recent introductory textbook for dynamic modelling in cell biology. Although targeted at the advanced undergraduate or graduate level, the teaching starts simply and requires very little prior knowledge from the reader. Each chapter considers a molecular based process in cell biology, of increasing complexity. Starting with a brief summary of the biological question, each section introduces very clearly the models, their variables and the differential equations describing their evolution. The bulk of each chapter is usually a description of a mathematical tool and its use to analyse the behaviour of the equations. Comparison with experiments and numerous exercises are included, together with commented references for further reading on the biological matter or the mathematical techniques used. The result is a very didactic, easy to read and excellent introduction to the subject.
The title of the book might be a little misleading, as the reader will learn more about the mathematical treatment of differential equations than about their numerical treatment. Exact solutions are given when they exist and many analytical techniques such as phase plane, stability analysis or systematic ways to simplify a system of equations are described. For example, the Michaelis-Menten equation for enzyme kinetic is derived using asymptotic analysis. This emphasis on mathematical analysis is an appropriate choice for a textbook, simply because it reflects reality: careful mathematical analysis is usually a pre-requisite to any successful numerical treatment. The first half of the book deals with whole cell models in which molecular species are implicitly considered as uniform in space. This leads to ordinary differential equations in time, for which many ready-made numerical solvers are available, some with no charge. Calcium waves and molecular motors are used to introduce diffusion processes and spatial variations, and lead to partial differential equations. Since ready-made solvers are not so easily available in this case, the authors present simple numerical schemes of educational rather than practical value.
Based on lectures given by Joel Keizer for many years at U.C. Davis, the book is remarkable for its pedagogical clarity. Without any long in-depth treatments, the sum of all the chapters will introduce the reader to many important mathematical and numerical tools such as stability or bifurcation analysis, the diagram analysis of equilibriums, the treatment of stochastic equations, Monte Carlo or Gillepsies methods for simulating chemical reactions, etc. Mastering all the techniques illustrated in the book will require additional reading, such as those listed in the appendix. The book is strong in presenting the in-action techniques, with state-of-the art models of realistic biological situation, where their usefulness is easily appreciated. Another strength of the book is to provide examples of the comprehensive modelling, from the initial descriptive molecular model to the full analysis of the equations. The contributors are active researchers who describe their active research, with topics such as oscillation in biological networks, or the modelling of molecular motors. Many modelling approaches presented in the book can be easily adapted or serve as templates to other biological problems.
The mathematically-able should find Computational Cell Biology a nice complement to the classical biology textbook. The subjects presented illustrate what mathematical modelling can bring to the biological sciences. Although not complete, the introduction is more concise than the scientific literature. Modelling in biology has traditionally been done by people coming from other disciplines. We feel that this book offers a new opportunity, by allowing biologists or graduate students entering biology to easily develop their mathematical skills. Computational Cell Biology is an attractive introduction to a number of mathematical techniques whose existence is simply unknown to many biologists. The remarkable clarity of the presentation makes it an unique self-teaching tool for scientists who would like to model their own experimental data, or to be able to appreciate modelling. Computational Cell Biology is a modern and valuable textbook full of simple yet useful analytical and numerical knowledge. It will please the mature scientist by its topics, and the student by its didactic style. Coming from a strong teaching practice, it is also the perfect support for a lecture series on mathematical modelling in biology.
Francois Nedelec, EMBL-Heidelberg, GERMANY