Computational Molecular Biology: An Introduction [Paperback]

Peter Clote (Author), Rolf Backofen (Author)

Book Description

ISBN-10: 0471872520 | ISBN-13: 978-0471872528 | Publication Date: September 22, 2000 | Edition: 1

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.

* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self-study
* Source code of programs available on a Web site

Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.

Editorial Reviews

Review

"...much needed introductory level text on the subject..." (La Doc STI, July 2000)

"...very concise and compact..." (Mathematical Reviews, 2002h)

From the Back Cover

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self study
* Source code of programs available on a Web site
Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.

Product Details

• Paperback: 300 pages
• Publisher: Wiley; 1 edition (September 22, 2000)
• Language: English
• ISBN-10: 0471872520
• ISBN-13: 978-0471872528
• Product Dimensions: 9.6 x 6.6 x 0.7 inches
• Shipping Weight: 1.3 pounds (View shipping rates and policies)
• Average Customer Review: 2.3 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #162,747 in Books (See Top 100 in Books)
  • #22 in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics

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Customer Reviews

Most Helpful Customer Reviews

33 of 33 people found the following review helpful:

3.0 out of 5 stars Interesting but not very good for beginners, November 22, 2000

By A Customer

This review is from: Computational Molecular Biology: An Introduction (Paperback)

This is an unusual book. The authors obviously have not been aquinted with biomolecular sequence analysis and fail to give state-of-the-art references to research work in this field. The same comment applies to the description of applications of Shannon communication theory to DNA and protein sequence analysis. The enormous impact of these applications in the 1970s, 1980s and 1990s is not reflected in the book and one could wonder why the authors bother to write of Shannon theory at all. In addition to the above misgivings the authors decided to confuse the reader by including a discussion of quite controversial relationship between Shannon entropy and thermodynamic entropy. Both computational and laboratory biologists will not benefit from this kind of confusion. Mathematicians and computer scientist will probably be mislead by a superficial treatment of this quite intricate topic. Physicists and chemist will probably be able to sort out useful information from over-interpretations but they may wonder why this issue is discussed in a computational biology text.

Despite the above critique I like the book. Organization of this text is interesting and distinctly different form other books in the field. Chapters on sequence alignment and phylogenetic trees are most interesting and original. They should probably be read in conjunction with more systematic textbooks such as Gusfield's "Algorithms on strings, trees and sequences" or Li's "Molecular evolution." Despite many misgivings (see the beginning paragraph of this review) the mathematical primer (chapter 2) is very much worth reading for its originality and compactness. Particularly sections about probability distributions and combinatorial optimization can be useful for non-mathematicians and interesting for those who are mathematically literate. However, care should be exercised (see the beginning paragraph) while reading sections about entropy and about optimality of the genetic code. Chapter 1 about principles of molecular biology is not very good for non-biologists because it is too compact. Chapter about structure prediction is also too compact to be either understandable to non-specialists or enjoyable by the experts. If the authors' ambitious approach was to be sustained, this chapter should probably be expanded to the size of entire book. Exercises at the end of every chapter of the book are interesting and worth the reader's attention. It would probably be good to have access to solutions of all exercises but it is a minor problem.

In summary: it is an interesting book but it should be read in conjunction with other texts. It should not be recommended to the beginners in computational biology. Mathematically seasoned readers will enjoy reading selected parts of this book. It would be nice if the publisher could consider lowering price of this book (already in paperback.)

12 of 13 people found the following review helpful:

2.0 out of 5 stars Unsuitable for its stated purpose., March 20, 2001

By 

Brendan LeFebvre - See all my reviews

This review is from: Computational Molecular Biology: An Introduction (Paperback)

The book purports to be a "self-contained introduction" to computational biology. It fails on both counts due to its excessive ambition, its opaque pedagogy, and a large number of significant typographical errors, such as entire subroutines missing from pseudocode examples. Undergraduates seeking an accessible survey are advised to look elsewhere.

That said, the mathematical rigor of the text makes it ideal for students who have moved beyond the need for accessible surveys and wish to improve their fundamental understanding of the field.

3 of 5 people found the following review helpful:

2.0 out of 5 stars Don't start with this book, February 12, 2004

By A Customer

This review is from: Computational Molecular Biology: An Introduction (Paperback)

In general I agree with the two previous reviews.

This book is not very good as an introduction. First read some other book such as Setubal and Meidanis, "Introduction to Computational Molecular Biology"; or Krane & Raymer, "Fundamental Concepts of Bioinformatics". These books have more readable narrative and examples.

The writing in this book is obtuse. It is written like an advanced abstract math book, not like an ostensibly applied science book. The notation is unnecessarily intricate. Even though it says "Introduction" in the title, there are very few tutorial examples. This is just for mathematicians/computer scientists: no biologist I have ever known would/could read this and really understand the algorithms.

This book does, however, have one of the more complete detailed descriptions of various algorithms used for sequence matching, etc. If you have read some other books and are looking for more details on algorithms, then this is your book. But I'm still waiting for THE ultimate Computational Biology book!