UK Nonlinear News, November 2002
Springer Verlag, 2002
210 figures. XX. 468 pages.
Hardcover. ISBN 0 387 95369 8
Before his untimely death in 1999, Joel Keizer had begun writing what he intended to be an introduction to modelling in cell biology, with particular emphasis on the computational aspects. Keizer was one of the foremost cellular modellers in the world. His research contributed some of the most important insights in the field of theoretical cell biology, particularly in the study of bursting oscillations and of calcium dynamics. He was truly a giant in the field, upon whose shoulders the rest of us are proud to stand. It was thus with some anticipation that we awaited the publication of his book, which, tragically, he was never able to complete.
Fortunately for us, however, Christopher Fall, Eric Marland, John Wagner (three students of Keizer) and John Tyson (a long-time colleague of Keizer) have adapted Keizer's notes and edited the introduction to computational cell biology which is the subject of this review. Many of the best-known names in the field have contributed chapters (a total of 17 authors in all) resulting in a book of which Keizer would have been proud, a book that stands as a worthy testament to Keizer's enormous contribution to theoretical cell biology.
The authors begin with a discussion of ionic models such as the Morris-Lecar model and the Hodgkin-Huxley model, and then discuss models of active and passive transport. After a discussion of how and why one should separate time scales in a model they move on to whole-cell models, including both closed-cell and open-cell models, and then discuss models of intercellular and synaptic communication.
The second half of the book has a markedly different flavour as the introduction of spatial and stochastic models requires a significantly higher level of mathematical expertise. An introduction to spatial modelling and the diffusion equation is followed by chapters on calcium waves, biochemical oscillations, the cell cycle, stochastic gating of ion channels, and molecular motors.
Throughout the book the theoretical discussions are accompanied by extensive use of the program XPPAUT  and the program itself is discussed in detail in Appendix B. XPPAUT is designed for the convenient solution of ordinary differential equations and simple partial differential equations, and includes an implementation of AUTO . AUTO is used extensively by many researchers and students in a wide range of fields, and XPPAUT contains a particularly user-friendly version. In general I am not a great fan of books that depend too heavily on a particular piece of software, but I think that it works in this case. The simple way in which the models can be implemented should encourage students to try simulating the models for themselves.
It is not so easy to identify a target audience for this book, as the required level of mathematical ability varies so radically from the beginning of the book to the end. For instance, it begins by carefully deriving the solution to the equation for simple exponential growth, and ends by discussing the Langevin equation and the Smoluchowski model. It is essentially, and openly admitted to be, two books in one. The first part of the book would be suitable for a typical undergraduate course in mathematical cell biology, including students from the biological sciences with little formal mathematical training. The second half of the book, on the other hand, is suitable only for much more advanced students, while the last few chapters are at a level suitable for graduate courses in mathematics departments.
Comparisons to other books in the field inevitably arise. Mathematical Models in Biology, by Edelstein-Keshet , is at a more elementary level overall, with more detailed explanations of the underlying mathematics, but is organised according to mathematical discipline rather than physiological topic, using examples from a broader range of biological topics than just cell biology. Mathematics in Medicine and the Life Sciences, by Hoppensteadt and Peskin , covers rather different topics with only relatively little attention given to cell biology, and, again, is overall at a much lower level. Mathematical Biology, by Murray , again covers rather different biological topics.
In style and content, this book is closest to Mathematical Physiology, by Keener and Sneyd . The latter covers a wider range of topics in cellular and systems physiology, at about the same level of mathematical sophistication, but does not address all the topics discussed in Computational Cell Biology. For instance, the chapters on ion channel gating and on molecular motors, have no counterparts in Keener and Sneyd. Even in the chapters where there is considerable overlap, as in the chapters on electrical excitability or calcium oscillations and waves, the present book takes a markedly different approach than that of Keener and Sneyd.
Although the wide range of mathematical sophistication does not make it a uniformly comfortable book for all readers, some of the advanced topics (the stochastic modelling of ion channels, for one) are superb expositions and I would not like the book to be without them. Thus, I can hardly object to the book's dual nature. And overall, Computational Cell Biology presents us with some elegant presentations of fundamental topics. Many of the chapters are absolute gems. As well as the chapter on ion channel gating, the chapter on cell cycle control stood out as a masterpiece, the best exposition of that topic I have seen anywhere. It seems a little unfair to single out only two chapters for particular praise, as all the chapters were excellent, but such is the unfair nature of reviews.
In short, Computational Cell Biology is a valuable addition to the literature, filling a number of gaps and presenting the material in a way that will be useful for students. It will have a place on my bookshelf, and in my required reading list.
UK Nonlinear News thanks Springer-Verlag for providing a review copy of this book.
A listing of books reviewed in UK Nonlinear News is available.