Introduction to Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics)

Book Description

Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences. This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences. Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.

Customer Reviews:

A modern classic.......2003-10-15

The first name people learn in bioinformatics is the Smith-Waterman algorithm. Some people never learn anything else. This is by that Waterman. Although written in 1995, it still has some of the best discussion I've seen on the topics it addresses.

The first few chapters deal with the "digest problem," reconstructing a DNA or protein sequence from the fragment sizes of enzyme digests. The technique is not used as much now as it was then, but it's always good to know the background of modern techniques.

The digest problem doesn't stand alone, though. It introduces concepts - islands, anchors, etc. - that still matter. The problems in reconstructing molecules from digests yield the same kinds of intermediate results and the same ambiguities that arise in modern sequencing. As Waterman advances the discussion, shotgun sequencing appears as a logical extension, at least mathematically, of digest assembly.

Sequence assembly involve end matching, perhaps in the presence of sequencing errors. That introduces the topic for which Waterman's name is famous, approximate string matching. The next few chapter progress through dynamic programming and multiple alignments. The logical connections between the techniques shown are so tight that chapter boundaries are almost artificial. It was a real pleasure to see the computational and practical relationships laid out.

The final topics, RNA structure and phylogenetic trees, lack the continuity that characterized the first dozen chapters. The RNA structure may be the weakest chapter in the book, but still a very competent introduction.

Throughout, Waterman emphasizes mathematical rigor without insisting on uninformative theorems. Every topic is presented in rich detail, with special attention to scoring and background models. Perhaps there are newer discussions of some topics. I don't know of any clearer discussions, though. Best, I think, is how Waterman prepares the reader to ask all the right questions in any future discussion: what are the elements of the computation, how can elements be recombined, how good is a result, and how does the result stand out from the statistical background.

The final chapter is what a bibliography should be. It doesn't just list authors, titles, and dates of publication. It actually discusses the contribution that each source made to this book. Rather than leave the reader to wander aimlessly among obscure titles, Waterman shows which sources are most informative on which topics. I wish more authors took the time for such commentary.

This is a book worth having. It covers topics that I haven't seen elsewhere, and shows how many different topics relate to each other. It is rigorous without giving distracting detail. Most of all, it keeps the biology in sight of all calculations. Some authors seem to forget that anything exists but the arithmetic; Waterman puts the math clearly in the service of its subject. I enjoyed it immensely, and look forward to applying its content in my own research.

Packed full of good information.......2000-08-13

This book gives a good survey of the different techniques employed by computational biologists. After a brief review of molecular biology in Chapter 1, the author treats the mathematical modeling of restriction maps in Chapter 2 using graph theory. His presentation is somewhat hurried, but he does give references and gives the reader three exercises at the end of the chapter. Multiple maps are treated in Chapter 3, wherein the author first makes use of probability theory, via the Kingman subadditive ergodic theorem. The proof is omitted but the author does a good job of explaining its use in studying the double digest problem (DDP). The best part of this chapter is the author's explanation of the difficulties of using Kingman's results for solving the DDP, and goes on to discuss multiple solutions of the DDP. Graph theory is again used in the discussion. This sets up the discussion in Chapter 4, which outlines algorithms for the DDP. The author gives a very compact introduction to P- and NP-complete problems in the theory of computation, then proves that DDP is NP-complete. The author does a good job of discussing subsequent approximate methods used for the DDP, such as simulated annealing. Markov chains are introduced in the book here for the first time, but due to the shortness of the presentation, the reader should do outside reading as a back-up. The author does a great job of explaining the difficulties if measurement error is introduced in the DDP at the end of the chapter. Cloning is discussed in Chapter 5, with tools from probability theory used to deal with partial digest libraries. The chapter is really short though, and the working the problems at the end of the chapter is essential for the understanding the results of this chapter. The author switches gears in the next chapter, wherein physical maps are discussed. The discussion is fairly detailed and interesting. Sequencing is discussed in the next two chapters, and the treatment is very good. Hashing is introduced here, and psedocode is given throughout. The very important method of dynamic programming is outlined in Chapter 9, which is beautifully written, and again pseudocode abounds throughout. Genetic mapping is left out though, but the this, the longest chapter of the book, is a detailed introduction to this area. The results in this chapter are used to study multiple sequence alignment in Chapter 10, wherein hidden Markov models are introduced for the first time. The discussion of these models is very curt, but there are other books and notes available if the reader needs further guidance. The best chapter of the book follows, which discusses probability and statistics for sequence alignment. The theory of large deviations is brought in, and the author does an excellent job of discussing this important, and powerful theory. The reader's level of mathematical sophistication is assumed to be a lot greater than the rest of the book in this chapter. Knowledge of measure theory and martingales are assumed here. The author uses the very powerful tool of relative entropy, so indispensable in other applications of probability. The problem set at the end of the chapter is challenging but working them through is definitely worth the time involved. The next chapter also uses some heavy guns from probability theory to study sequence patterns. The author returns to matter of a more empirical nature in Chapter 13, which deals with RNA secondary structures. The reader with a background in simple combinatorial theory should find the reading straightforward and informative. Continuous-time Markov chains are introduced in the next chapter to study trees and sequences. The treatment here is rather hurried, so again the reader should work the exercises at the end of the chapter. The book ends with a discussion of the literature and references. All in all a very nice book, worth the price, and worth spending time reading. The only minus might be the total omission of actual source code, but that really was not the intent of the book. Readers with a strong mathematical background will like the book, as well as anyone interested in going into the area of computational biology.

Principles and Practices of Unbiased Stereology: An Introduction for Bioscientists

Average customer rating:

Well written and straightforward
A fantastic book

Principles and Practices of Unbiased Stereology: An Introduction for Bioscientists
Peter R. Mouton
Manufacturer: The Johns Hopkins University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0801867975

Book Description

Beginning in the 1960s, scientists across a wide range of disciplines cooperated in developing unbiased--or assumption free--stereology, based on stochastic geometry and probability theory, as a way to estimate the parameters of irregularly shaped objects without introducing bias. In recent years these new estimation techniques, which were originally quite painstaking and time consuming, have made a great deal of headway in disciplines such as neuroscience, thanks in part to the development of computer software for their application. Prestigious journals and grant-giving organizations now require the use of unbiased stereology in the projects that they support, and this trend is expected to continue.

Principles and Practices of Unbiased Stereology will fill a need in the biomedical community as a clear, user-friendly introduction to this area for the increasing number of scientists who need to learn these techniques for their research. The work moves logically from a discussion of the historical background of stereology to full explanations of terms, concepts,and tools, with the latter part of the manuscript devoted to typical stereology designs. An associated web site will feature color illustrations and video clips demonstrating stereological techniques.

Customer Reviews:

Well written and straightforward.......2005-07-01

Stereology refers to a set of methodological tools (e.g. mathematical, statistical, and geometric) for obtaining information about three-dimensional objects from two-dimensional profiles. This book provides the necessary theoretical and methodological framework for beginners in the field, by describing the basic `principles and practices' of stereology. It is primarily written for neuroscientists and biomedical scientists but geologists and other scientists from a variety of different disciplines will find it very useful. The first chapter offers a historical overview of the fundamental concepts of stereology and the events that led to the present era of modern stereology. Subsequent chapters deal with stereological techniques for quantifying first-order parameters (volume, length, surface area, number) as well as issues of experimental design, sampling strategies and possible sources of bias. The concise illustrations and figures that accompany the text help to promote better understanding of the abstract concepts. Complex formulations are avoided and the simple explanations that are provided make the entire book very easy to read and follow. Overall, this book is well-written and straightforward introduction to the basic principles of modern stereology.
Michail E. Kalaitzakis, Ph.D. D.I.C. Candidate, Faculty of Medicine, Department of Neuropathology, Imperial College London, University of London, UK.

A fantastic book.......2002-08-28

Principles and Practices fills a much needed niche-- this book excels at introducing beginners to stereology and is sufficiently advanced that more experienced scientists will find themselves using it for reference. The most interesting and useful feature of this book is its organization. Beginning with an historical perspective, the reader learns the importance of stereology as a separate field, as well as its recent impact on the biological sciences. The author does an excellent job of providing pertinent information instead of overwhelming with formulas and jargon. Dr. Mouton's treatment of bias and error is superb; I find myself referring to it frequently.

Synchronization And Control Of Chaos: An Introduction For Scientists And Engineers

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Synchronization And Control Of Chaos: An Introduction For Scientists And Engineers
J. M. Gonzalez-Miranda
Manufacturer: Imperial College Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 1860944884

Book Description

This book presents a comprehensive overview of the research and latest developments in the field of the dynamics of coupled and driven chaotic oscillators, aimed at a wide audience. Since 1990, there has been very active research devoted to the field, culminating in a considerable body of knowledge, while active research continues.

Book Description

Focusing on discrete models across a variety of biological subdisciplines, this introductory textbook includes linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction from DNA sequence data, genetics, and infectious disease models. Assuming no knowledge of calculus, the development of mathematical topics, such as matrix algebra and basic probability, is motivated by the biological models. Computer research with MATLAB is incorporated throughout in exercises and more extensive projects to provide readers with actual experience with the mathematical models.

An Introduction to Nonlinear Finite Element Analysis

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classic

An Introduction to Nonlinear Finite Element Analysis
J. N. Reddy
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Hardcover

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ASIN: 019852529X

Book Description

This book presents the theory and computer implementation of the finite element method as applied to nonlinear problems of heat transfer and similar field problems, fluid mechanics (flows of incompressible fluids), and solid mechanics (elasticity, beams and plates). Both geometric as well as material nonlinearities are considered, and static and transient (i.e. time-dependent) responses are studied. Although there exist a number of books on nonlinear finite elements that serve as good references for engineers who are familiar with the subject and wish to learn advanced topics or the latest developments, there is currently no book which is suitable as a textbook for a first course on nonlinear finite element analysis. This book fills the void in the market, providing a clear understanding of the concepts of nonlinear finite element analyses through detailed theoretical formulations and computer implementation steps, examples and exercises. In addition, the book serves as a prelude to more advanced books on the subject.

Customer Reviews:

classic.......2004-08-01

There is this one thing I know. When you are learning from a superior intelligence you can find that out pretty quick. My greed for calculus and math finds an apt expression here. The way he presents non linear finite elements is unparalled.

There is another thing I have learnt. The people who can explain things clearly, are the ONLY ones who know it. And they are not too many, when it comes to horribly complicated problems, besides. Math cuts out a lot of verbiage, in understanding a problem. A simple math problem when described in words could go on and on and on. If you have a apetite for calculus and a craving for numerical analysis, you would be a poorer man if you didnt have this book. If you dont, well you are not good enough to pick up finite elements anyhow, forget about non linear analysis.

One small note. This is just pertinent to me. I dont write code in fortran so parts of the book were not of much use to me. But the concept is laid out very well. I would have loved it if the code was in C++. But its okay. If I were to just copy code, that would be shabby learning anyhow. So it works out fine.

An Introduction to the Mathematics of Biology

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An excellent book for a beginner interested in math and bio

An Introduction to the Mathematics of Biology
Edward K. Yeargers , James V. Herod , and Ronald W. Shonkweiler
Manufacturer: Birkhauser
ProductGroup: Book
Binding: Hardcover

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Essential Mathematical Biology

ASIN: 0817638091

Book Description

The authors of this new textbook have adopted the philosophy that mathematical biology is not merely the intrusion of one science into another but has a unity of its own. The biology and mathematics are equal; they are complete and flow smoothly into and out of one another. Student response to this approach has been exhilarating to watch as standard, unexciting applications give way to problems of contemporary interest- HIV, genetics and aging, for example.

The book has several important features that the authors have developed from their classroom experience. First and foremost, it is designed to be comprehensible to students of biology as well as to students of mathematics and related physical sciences. No prior study of biology is necessary and only a year of calculus is required. The mathematics proceeds from simple to more complex concepts, and the biology proceeds from the population level down to the molecular level. This arrangement makes the material accessible to most biology majors and to most mathematics students near the beginning of their mathematical studies.

A unique feature of the book is the use of a computer algebra system, Maple, in parts of every chapter. This hands-on approach to computation provides a rich source of information through the use of "what-if" scenarios and thus allows students to grasp important biological and mathematical concepts in a way that is not possible without such technology. For students who do not have access to a computer algebra system, each topic is complete without the use of either numerical or symbolic equations. Graphic visualizations are provided for all the mathematical results.

The text has extensive exercises, problems and examples, along with references for further study. It will be of interest to any mathematics department that teaches mathematical biology. It also lends itself to self-study for more advanced mathematicians and scientists who wish to explore further this most exciting frontier in the applications of mathematics and computers to the natural sciences.

This text has been adopted at: Georgia Institute of Technology, Carnegie Mellon University, University of California at Los Angeles, California State University, Northeastern Illinois University, and University of Colorado.

Customer Reviews:

An excellent book for a beginner interested in math and bio.......1998-09-01

I found this book useful as a beginner in biology with a background in mathematics. There is an emphasis on the biology aspect, but the mathematics in fairly in depth.

Introduction to Mathematics for Life Scientists (Springer Study Edition Series)

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A Work of Art!
Over the top-6 stars
Absolutely Fantastic
An exceptionally useful and accessible book.

Introduction to Mathematics for Life Scientists (Springer Study Edition Series)
Edward Batschelet
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Mathematical Models in Biology: An Introduction

ASIN: 0387096485

Book Description

From the reviews: "...Here we have a book which we can wholeheartedly suggest. The mathematics is sound and pared to essentials; the examples are an impressive, well-chosen selection from the biomathematics literature, and the problem sets provide both useful exercises and some fine introductions to the art of modeling... Batschelet has written an introduction to biomathematics which is notable for its clarity - not only a clarity of presentation, but also a clarity of purpose, backed by a sure grasp of the field..." (Bulletin of Mathematical Biology).

"For research workers in the biomedical field who feel a need for freshening up their knowledge in mathematics, but so far have always been frustrated by either too formal or too boring textbooks, there is now exactly what they would like to have: an easy to read introduction. This book is highly motivating for practical workers because only those mathematical techniques are offered for which there is an application in the life sciences. The reader will find it stimulating that each tool described is immediately exemplified by problems from latest publications" (Int. Zeitschrift für klinische Pharmakologie, Therapie und Toxikologie).

This corrected, third edition differs from the preceding one essentially by the use of SI-units throughout the text.

Customer Reviews:

A Work of Art!.......2007-04-12

I very much agree with the previous reviews. This book is a rare gem, a treasure, truly a work of art.

It's the finest and most enjoyable mathematics book I've encountered to date, and that's taking into account the existence of many excellent math books. Certainly among books specifically on applied mathematics, this book is uniquely valuable.

The choice of topics, sequence of presentation, level of detail, use of examples, and clarity and elegance of exposition are all outstanding. Batschelet has crafted every chapter, section, paragraph, and even sentence with meticulous care and precision in the finest Swiss tradition.

If you've already studied mathematics through calculus, differential equations, probability, linear algebra, etc., this book is an especially good resource to review it all via a single coherent book.

To illustrate how much I cherish this book, I've bought two copies -- one to read, and the other in case the first is damaged and the book goes out of print.

If you love math, buy this book. If you just have an interest in math, buy this book and read it, and you may well come to love math.

Over the top-6 stars.......2006-05-30

This book is quite the best I've seen, the finest book on applied mathematics ever. This textbook puts to shame nearly all other attempts. Batschelet possesses a mastery of presentation that borders on genius. Chapter 7 --including basic concepts of logarithms-is worth the price alone. Book topics build progressively: Real numbers, sets,logic, relations and functions then power,periodic,exponential & logarithm functions; graphical methods, limits, differential & integral calculus; advanced log & hyperbolic functions,ordinary differential equations, multivariable functions; probability, matrices & vectors, complex numbers.Truly a pedagogical masterpiece.

Absolutely Fantastic.......1999-12-17

I purchaised this book while I was doing my Ph.D. I am happy that I now have a chance to write about it. Few books on mathematics are so enjoyable to read as this one. It explains concepts with amazing clarity. It guides you through all the necessary mathematics and will infact motivate you to take up mathematics as your major. This book should never go out of print. The author has made a long standing contribution by writing this book. There will be thousands of students who will be appreciate this book for generations. I would say buy it, with your eyes closed. You will never regret it.

An exceptionally useful and accessible book........1998-12-04

This is one of the few books I have come across that more often than not will contain the solution to those mathematical problems encountered in the practical pursuit of biological investigation. It is written in a clear and entertaining manner, with many footnotes containing points of historical or general interest. Too often mathematical texts are dry and inaccessible to the non-specialist; Edward Batschelet's book represents a refreshing oasis that has reassured many a reluctant mathematician among the ranks of the life sciences. Buy it!!

An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Average customer rating:

Good Theoretical GA Textbook
Not for beginners
An introduction and much more
A Great Introduction to Genetic Algorithms
Good introduction for such a short book

An Introduction to Genetic Algorithms (Complex Adaptive Systems)
Melanie Mitchell
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0262133164

Book Description

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.

The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Customer Reviews:

Good Theoretical GA Textbook.......2005-05-06

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

Not for beginners.......2004-02-04

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

An introduction and much more.......2004-01-26

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

A Great Introduction to Genetic Algorithms.......2002-12-07

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

Good introduction for such a short book.......2002-04-07

Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.

Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.

The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.

Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.

Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.

Book Description

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.

Customer Reviews:

Don't start with this book.......2004-02-13

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!

Unsuitable for its stated purpose........2001-03-21

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.

Interesting but not very good for beginners.......2000-11-23

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.

Book Description

Darwin's theory that our ancestors were apes caused a furore in the scientific world and outside it when The Origin of Species was published in 1859. Arguments still rage about the implications of his evolutionary theory, and scepticism about the value of Darwin's contribution to knowledge is widespread. In this analysis of Darwin's major insights and arguments, Jonathan Howard reasserts the importance of Darwin's work for the development of modern biology.

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