Computational Cell Biology (Hardcover)

by Christopher Fall (Editor), Eric Marland (Editor), John Wagner (Editor), John Tyson (Editor) "Over the past several decades, progress in the measurement of rates and interactions ot molecular and cellular processes has initiated a revolution in our understanding..." (more)
Key Phrases: open cell model, average rate equation, basal parameter values, Monte Carlo, Joel Keizer, John Rinzel (more...)

Editorial Reviews

Review
From the reviews: Nature Cell Biology, Vol. 5/2, February 2003  "The authors and editors of this volume are outstanding mathematical biologists. Computational Cell Biology introduces the principles, techniques, tools and insights of mathematical biology through detailed exposition of ion channels, calcium signaling, transporters, cellular endocrinology, gap junctions, cell cycle controls and molecular motors. If you want to see the tools of applied mathematics at work in some key areas of cell physiology and cell biology, this is the book to read. Your understanding of rate laws, ordinary and partial differential equations, numerical methods, phase portraits, stability analysis, Brownian ratchets, multiple time scales, bifurcations and stochastic processes will all be enhanced. You will have become significantly bilingual. The authors reach out valiantly from the blue of mathematical biology toward the red of experimental biology. Perhaps the time has finally arrived when experimental cell biologists can reach back and shake hands on an effective partnership. It's an epochal moment; illuminating the shadowy complexity of cell biology will surely require white light." BIOINFORMATICS "…a very didactic, easy to read and excellent introduction to the subject…the book is remarkable for it pedagogical clarity…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 modeling, from the initial descriptive molecular model to the full analysis of the equations…Many modeling approaches presented in the book can be easily adapted or serve as templates to other biological problems…The mathematically-able should find [the book] a nice complement to the classical biology textbook…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 modeling…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 modeling in biology." "This textbook address students of either mathematics or the biological sciences interested in the other topic. It aims to introduce to computational cell biology, i.e. the question, whether and how mathematical modelling can contribute to a description or understanding of cellular phenomena. … the book should be well readable and provide essential guide to the methodology of modelling in biology." (H. Muthsam, Monatshefte für Mathematik, Vol. 143 (4), 2004) "Computational Cell Biology has been carefully designed as a text for an introductory course in cellular dynamics, in which the students are to be drawn from both biology and applied mathematics. … Computational Cell Biology is an imaginatively conceived, carefully written, and well-edited book, which is strongly recommended as the text for advanced and early graduate courses in mathematical biology. Both students and teachers will enjoy learning from it, and future quantitative biologists and biomathematicians will remember it fondly." (Alwyn Scott, SIAM Review, Vol. 45 (2), 2003) "Joel E. Keizer was the pioneer in computational cell biology. This textbook represents the combined efforts of friends and colleagues to complete a task that Joel was unable to … end. In its pages one can easily sense the enthusiasm for this subject that he generated in others. It is a tribute that each chapter has been written by a well-recognized expert in the field. The result is a work that I believe should be on the desktop of all who appreciate good science." (John G. Milton, Mathematical Reviews, 2003 j) "Computational Cell Biology is a recent introductory textbook for dynamic modelling in cell biology. … The result is a very didactic, easy to read and excellent introduction to the subject. … the book is remarkable for its pedagogical clarity. … Another strength of the book is to provide examples of the comprehensive modelling … . It will please the mature scientist by its topics, and the student by its didactic style. … it is also the perfect support for a lecture series … ." (Francois Nedelec, Bioinformatics, July, 2003) "Computational Cell Biology presents us with some elegant presentation of fundamental topics. Many of the chapters are absolute gems. … 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." (James Sneyd, UK Nonlinear News, November, 2002)

Product Description
This textbook provides an introduction to dynamic modeling in cell biology, emphasizing computational approaches based on realistic molecular mechanisms. It is designed to introduce cell biology and neuroscience students to computational modeling, and applied mathematics students, theoretical biologists, and engineers to many of the problems in dynamical cell biology. This volume was conceived of and begun by Professor Joel Keizer based on his many years of teaching and research together with his colleagues. The project was expanded and finished by his students and friends after his untimely death in 1999.

Carefully selected examples are used to motivate the concepts and techniques of computational cell biology, through a progression of increasingly more complex and demanding cases. Illustrative exercises are included with every chapter, and mathematical and computational appendices are provided for reference. This textbook will be useful for advanced undergraduate and graduate theoretical biologists, and for mathematics students and life scientists who wish to learn about modeling in cell biology.

Royalties from this book will be donated to the Joel E. Keizer memorial endowment for collaborative interdisciplinary research in the life sciences.

Product Details

• Hardcover: 488 pages
• Publisher: Springer (February 15, 2005)
• Language: English
• ISBN-10: 0387953698
• ISBN-13: 978-0387953694
• Product Dimensions: 9.3 x 7 x 1.2 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
• Amazon.com Sales Rank: #155,473 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

17 of 17 people found the following review helpful:

5.0 out of 5 stars An excellent overview, March 31, 2004

By	Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews (TOP 100 REVIEWER)    (REAL NAME)

As a field of applied mathematics, computational biology has exploded in the last decade, and shows every sign of increasing in the next. This book overviews a few of the topics in the computational modeling of cells. I only read chapters 12 and 13 on molecular motors, and so my review will be confined to these.

Nanotechnology could be described as an up-and-coming field, but in the natural world one can find examples of this technology that surpass greatly what has been accomplished by human engineers. The authors begin their articles with a few examples of natural molecular machines, including the "rotary motors" DNA helicase and bacteriophage, and the "linear motor" kinesin, the latter they refer to as a "walking enzyme". Important in the modeling of all these is the theory of stochastic processes in the guise of Brownian motion, which the authors hold is the key to understanding the mechanics of proteins. In chapter 12 they give a detailed overview of the mathematical modeling of protein dynamics, followed in chapter 13 by an illustration of the mathematical formalism in the bacterial flagellar motor, a polymerization ratchet, and a motor governing ATP synthase.

To the authors a molecular motor is an entity that converts chemical energy into mechanical force. The production of mechanical force though may involve intermediate steps of energy transduction, all these involving the release of free energy during binding events. But due to their size, molecular motors are subjected to thermal fluctuations, and thus to model their motion accurately requires the theory of stochastic processes. Thus the authors begin a study of stochastic processes, restricting their attention to ones that satisfy the Markov property. Starting with a discrete model of protein motion as a simple random walk, the authors show that the variance of the motion grows linearly with time, which is a sign of diffusive motion. The partial differential equation satisfied by the probability distribution function, in the continuous limit where the space and time scales are large enough, is left to the reader to derive as an exercise.

The authors then consider polymer growth as another example of a stochastic process, a kind of hybrid one in that it involves both discrete and continuous random variables, the position of the polymer being continuous, while the number of monomers in the polymer is discrete. The authors derive an ordinary differential equation for the probability of there being exactly n polymers at a particular time. From this they show how to obtain sample paths for polymer growth and give a brief discussion on the statistics of polymer growth.

Attention is then turned to the modeling of molecular motions, with the first example being the Brownian motion of proteins in aqueous solutions. The (stochastic) Langevin equation is given for the motion of the protein, both with and without an external force acting on the protein. To find a numerical solution of this equation is straightforward, as the authors show. But they caution however that simulation of this solution on a computer is liable to introduce spurious results, and so they derive the Smoluchowski model, a somewhat different way of looking at random motion via the evolution of ensembles of paths. In this formulation the Brownian force is replaced by a diffusion term, and the external force is modeled by a drift term.

The authors then consider the modeling of chemical reactions, which supply the energy to the molecular motors. Because of the time scales involved in these reactions, a correct treatment of them would involve quantum mechanics, but the authors use the Smoluchowski model. The simple reaction model they consider involves a positive ion binding to negatively charged amino acid, and using as reaction coordinate the distance between the ion and the amino acid, study the free energy change as a function of the reaction coordinate.

The numerical simulation of the protein motion is then considered in much greater detail, using an algorithm that preserves detailed balance. This involves converting the problem to a Markov chain and a consideration of the boundary conditions, which the authors do for the case of periodic, reflecting, and absorbing. Euler's method is used to solve the resulting equations for the Markov chain, and after dealing with issues of stability and accuracy, the Crank-Nicolson method is used. The last few sections of the chapter are devoted to the physics of these solutions and the authors give some intuitive feel for the entropic factors and energy balance on a protein motor.

In the last chapter of the book, the considerations in chapter 12 are applied to concrete molecular motors. The first one examined is a model for switching in a bacterial flagellar motor, which involves the protein CheY as a signaling pathway. The binding of CheY to the motor is modeled as a two-state process, with the binding site being either empty or occupied. The resulting set of coupled differential equations for the probabilities is solved for when the concentration of CheY is constant. An expression for the change in free energy is obtained, and the authors give a discussion of the physics in the light of what was done in the last chapter. The switching rate is computed, along with the mean first passage time.

Some other examples of molecular motors are also discussed, including the flashing racket, the polymerization ratchet, and a simplified model of the ion-driven F0 motor of ATP synthase. This latter motor is fascinating, since it describes the electrochemical energy involved in mitochondria for the production of ATP. The authors do a nice job of showing how the techniques of chapter 12 are used to solve this model, and also give an analytical solution for a certain limiting case.