Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology)

by Pavel A. Pevzner

Publisher: MIT Press; 1st Edition
ISBN: 0262161974

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Book Info

A textbook covering a large range of algorithmic and combinatorical topics and showing how they are connected to molecular biology and biotechnology. This material is accessible to computer scientists without biological training and to biologists with a limited background in computer science.

In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple manner. This makes the material accessible to computer scientists without biological training, as well as to biologists with limited background in computer science.

Computational Molecular Biology series: computer science and mathematics are transforming molecular biology from an informational to a computational science. Drawing on computational, statistical, experimental, and technological methods, the new discipline of computational molecular biology is dramatically increasing the discovery of new technologies and tools for molecular biology. The new MIT Press Computational Molecular Biology series provides a unique venue for the rapid publication of monographs, textbooks, edited collections, reference works, and lecture notes of the highest quality.

About the Author

Pavel A. Pevzner is Professor in the Departments of Mathematics, of Computer Science, and of Biological Sciences at the University of Southern California.

Customer Reviews

As others have noted, the premise that this book is for beginners from either the computational or the biological field is flawed...unless one's definition of beginner is a lot more advanced than mine.

For example even chapter one throws out terms like "recombination" and electrophoresis. without enough explanation for the biology newbie, IMO. Heck, for someone truly new to biology, a bit of time explaining what a chromosome is is probably time well spent.

And for the person coming from a pure biology background, some of the mathematics will definitely be a problem unless they have a decent understanding of combinatorics and discrete mathematics. And that "computational biology without formulas" blurb on the back cover should be read as "not as many formulas as I could have included if I really wanted", rather than "no formulas at all". There are equations galore in this book, rest assured of that.

That said, if a person *does* have the necessary background to make the material accessbile, then the book is definitely worth the purchase. The book's failure is in defining its target audience, not in the material presented.

While this is certainly the do-loop of computational biology the reader would question the assertion that this book provides a common link (no pun) between the biologists need for computational expertise and the programmer's need for biological insight. In either case a solid basis in Discrete Mathematics goes along way here (usually a required course for computer science majors). This reader thinks a similar required course in genetics should be made for engineers to reduce their reductionistic tendencies. However the distinction between these lines grows narrower with each new computer chip. None the less the book is well written, and easy to read (as Discrete Math stuff goes). This book is not for beginners in either Combinatorics or genetics and the last part of the book poses many current questions that as the author says, "are just currently being answered". This book already assumes you know about such things as NIH, PDB, Chime, Isis, NCIB, docking, etc. For those less adapt at programming (myself) the following alternatives are fun, useful and to the point. Both trees and networks can be easily set up in MathCad using their built in resource center add-ins for Combinatorics and Set Theory. They also provide a Traveling Salesman routine in Numerical Recipes that can be applied directly to the problems in Pevzner's book. (Although remembering that most optimization algorithms provide only the most probable 100 out of 2 million it is still fun!). Most of the mappings and node process familiar to Discrete Math can be solved using Mathcad and some sort of adjacency matrix combination. (Including the four-color mapping problem). This provides the basis for most nodal mappings. For the more daring the adjacency matrices can be run through Matlab's GUI's decompositions and analyzed using their optimization toolbox. Currently I'm investigating the Hidden Markovian chains using the Frame advance feature of Mathcad applied to 2D cspline- intercept graphing and updating by frame iteration. This book is for the serious student or solid course material in a related field, and while probably not rated in top ten novels of 2000 certainly rates five mouse clicks from this reader.

The title is somewhat misleading because the book is primarily devoted to combinatorial methods that could be used in genome sequencing and genomics. The selection of methods is arbitrary and does not seem to be dictated by either pedagogical or scientific vision. It mainly reflects the author's own work and interests. Contrary to what the editorial review states I find this text quite abstract and formal. I like the book very much but I don't think it should be recommended to the beginners in computational biology. Readers who have a taste for mathematics and a strong background in combinatorics could benefit the most from reading this book. Anybody who looks for a textbook-level guidance in computational biology should probably rely on better designed texts such as Don Gusfield's "Algorithms on strings trees and sequences" or "Biological sequence analysis" by Durbin and co-authors. However, the readers who are interested in mathematics behind designs of DNA arrays (chapter 5) or in mathematical treatment of genome rearrangements (chapter 10) should certainly read this book in detail.

If you want to understand what is INSIDE those nice software tools available to molecular biologists and now on the web you have to study this book. It's a little more advanced than Gusfield's in some aspects, and more research oriented. Of course it does not cover uniformly all areas of computational biology: if you know Pavel's work, it would be very easy to predict the content of the chapters. For example, more than 50 pages are dedicated to genome rearrangement, but only 10 on multiple sequence alignment. Anyway, this is good, because we can learn about alignment from many other books, in particular the one by Gusfield. I strongly recommend this book to anyone interested in this fascinating field of Science.