Computational
Cell Biology
Author(s):
Christopher P. Fall, Eric S. Marland, John M. Wagner and John J. Tyson, Editors
ISBN 0387953698
More Information
Springer Verlag, 1st edition (July 9, 2002)
Hardcover: 488 pages
REVIEW
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 MichaelisMenten 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 prerequisite 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
readymade 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 readymade 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 indepth 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 Gillepsie’s
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 inaction techniques,
with stateofthe 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 mathematicallyable 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 selfteaching 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, EMBLHeidelberg, GERMANY
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