Molecular Modeling and Simulation

Book Description

This book evolved from an interdisciplinary graduate course entitled Molecular Modeling developed at New York University. Its primary goal is to stimulate excitement for molecular modeling research while introducing readers to the wide range of biomolecular problems being solved by computational techniques and to those computational tools. The book is intended for beginning graduate students in medical schools and scientific fields such as biology, chemistry, physics, mathematics, and computer science. Other scientists who wish to enter, or become familiar, with the field of biomolecular modeling and simulation may also benefit from the broad coverage of problems and approaches. The book surveys three broad areas: biomolecular structure and modeling: current problems and state of computations; molecular mechanics: force field origin, composition, and evaluation techniques; and simulation methods: geometry optimization, Monte Carlo, and molecular dynamics approaches. Appendices featuring homework assignments, reading lists, and other information useful for teaching molecular modeling complement the material in the main text. Extensive use of world wide web resources is encouraged, and additional course and text information may be found on a supplementary website. Some praise for Tamar SchlickÂ¿s Â¿Molecular Modeling and Simulation: An Interdisciplinary GuideÂ¿:||"The interdisciplinary structural biology community has waited long for a book of this kind which provides an excellent introduction to molecular modeling.Â¿|Â¿Harold A. Scheraga, Cornell University||"A uniquely valuable introduction to the modeling of biomolecular structure and dynamics. A rigorous and up-to-date treatment of the foundations, enlivened by engaging anecdotes and historical notes.Â¿|Â¿J. Andrew McCammon, Howard Hughes Medical Institute, University of California at San Diego||"I am often asked by physicists, mathematicians and engineers to recommend a book that would be useful to get them started in computational molecular biology. I am also often approached by my colleagues in computational biology to recommend a solid textbook for a graduate course in the area. Tamar Schlick has written the book that I will be recommending to both groups. Tamar has done an amazing job in writing a book that is both suitably accessible for beginners, and suitably rigorous for experts.Â¿|Â¿J.J. Collins, Boston University

Customer Reviews:

Outstanding introduction.......2004-05-13

... not only to molecular modeling, but to some of the subtleties of DNA and protein behavior and geometry, too.

This book's focus is generally on interactions with large molecules, DNA and proteins, although it does discuss small molecules (drugs, a few dozen to a few hundred atoms) too. That means that it skips most of the quantum mechanical modeling of more advanced computational chemistry texts.

Nothing is lost, because Schlick covers her chosen topic (molecular modeling and dynamics) in such detail. She starts with a very clear discussion of the structure of large biomolecules, with emphasis on the features that need quantitative description for modeling. That covers protein structure at ever level. It also covers DNA/RNA structure in the best detail I've ever seen. The double-helix is the just the starting point. There are alternative helix forms, non-standard binding between nucleotides, and asymmetries caused by nucleotide composition. The next chapters describe the geometric model and, briefly, the forces acting between atoms.

The second half of the book gets down to the nuts and bolts of modeling. This includes numerical techniques, minimization, sampling and Monte Carlo techniques, and the start of dynamics. Schlick attacks some of the nasty points of the calculations, such as modeling of forces that act on very different time scales. As with the simpler material, the development is clear, descriptive, and free of pointless theorems. The meticulous reader should come away able to implement most or all of the techniques described. The level of presentation is consistent and approachable. I think freshman physics should be enough preparation for most students to get most of the value out of the discussion.

The book is written with clarity as a top priority. The glossary is in the front, making sure that the reader knows it's a first-class part of the text. After that, every chapter starts with a list of the mathematical symbols and variables used and a one-line description of each. These are small things, but they increase the book's readability immensely. The illustrations are generally informative enough. On the whole, though, they don't seem quite up to the level of the textual and mathematical presentations.

I needed a crash course in the mathematical techniques used for describing molecular structure and behavior. I should have read this book first - its clarity and thoroughness would have saved me a lot of time. After this one, I can now go back and reread the more complex texts with more hops of understanding. Do yourself a favor and read this one first.

A long expected book in molecular modeling is finally here.......2004-02-17

I highly recommend Professor T. Schlick's book. It is beautifully written with many examples and great illustrations. The book is truly interdisciplinary; it covers, in good depth, both the biological and mathematical aspects of computational structural biology. Most chapters start with an amenable introduction and finish with "hands-on" recommendations and future challenges. I was particularly pleased with the level of detail in each chapter (in particular those that show the reader the advantages and pitfalls of the different methods presented). My colleague Mariel Vazquez and I used this book in the design and preparation of our "Special topics in Mathematics" course at the UC Berkeley Mathematics Department during the Spring of 2003.

This upper-level undergraduate/lower-level graduate course was centered on mathematical and computational models of the three dimensional structure of DNA, and DNA topology. We found Professor T. Schlick's book very useful in our class preparation. In particular we covered chapter 5 (DNA structure) completely, sections 3 and 4 from chapter 7 (basic principles and formulation of atomic interactions in molecular mechanics), and several sections or subsections from chapters 8 and 9 (force terms used in molecular dynamics simulations). We also covered most of the material in chapter 10 (Multivariate Minimization), and gave a brief introduction to chapter 11 (Monte-Carlo techniques) and chapter 12 (Molecular Dynamics algorithms).

Chapter 5 starts with a very amenable and brief introduction that relates DNA with other biological processes and describes some of the challenges in studying DNA structure. It continues describing the basic building blocks of DNA. The author wisely spends some time defining the nomenclature for each of the atoms, angles and bonds that form these basic blocks. The following sections teach the reader what parameters are relevant for describing a DNA double helix and how they characterize the A, B and Z- forms of DNA. Illustrations in this chapter are particularly helpful.

Although our course's approach to DNA supercoiling was different that the one in the book I found particularly useful some illustrations in chapter 6 and movies (to be found in her webpage) that Prof. Schlick's group has developed over the years. In brief, chapter 6 is a study of more complex structures and behavior of DNA (such as structural role of the DNA sequence, DNA-protein interactions, and higher order organization of DNA -i.e. DNA supercoiling and histone-DNA interactions). This chapter can be a good source for short research projects (e.g. final projects).

Chapters 7, 8 and 9 describe the basic concepts in molecular mechanics. From sections 7.3 and 7.4 I found of interest how the author addresses the problem of the system size (i.e. number of interacting molecules) and some of the details that the author gives for modeling the geometry of atomic interactions. At the end of the chapter (section 7.4.3) interested readers can find some of the limitations of current approaches. Chapters 8 and 9 describe in depth the force fields and how to implement them. Chapter 9 also illustrates with clarity how to implement periodic boundary conditions and the advantages of using different lattice models.

Chapter 10 describes a number of familiar methods for energy minimization (i.e. steepest descent, conjugate gradient, etc....). We used sections 10.1 to 10.4 and section 10.5.2 (conjugate gradient). I found the Hessian patterns shown in figures 10.4 and 10.5 and the minimization trajectories shown in 10.10 very pedagogical. As in previous chapters the author finishes with practical recommendations and future challenges.

We left chapter 11 (Monte Carlo methods) for last in the course and discussed chapter 12 (molecular dynamics) first. As in previous chapters the author gives a very nice introduction (section 12.1 and 12.2) and covers the basics on simulation protocols in sections 12.3 and 12.4. Section 12.4 describes the basic integration algorithms such as leap-frog, verlet, etc... Figure 12.3 was revealing for the students as it compares the time scales in biological systems.

Chapter 11 (Monte-Carlo methods) provides a very comprehensive introduction to Monte-Carlo methods. We found particularly useful some of the subsections of random number generation and the treatment of Importance sampling and Markov chains in section 11.5.

As mentioned earlier we were particularly delighted with the amount of details given in each topic. For example chapters 7 and 8 provide all the formalism needed for the problems of molecular mechanics. In section 8.4 (bond angle potential) the author highlights the differences (both formally and by figures-see figure 8.4) between different formulations of the problem (see also figure 8.6). In Chapter 10 the author describes minimization algorithms in detail and shows some of the patterns that one observes in the Hessian associated to minimization functions of biological structures (see figs. 10.4, 10.5 and 10.11). She also makes very detailed comparisons between the different minimization methods (see figs 10. 2, 10.10). In chapter 12 she compares the different methods and initial conditions for the algorithms discussed (figs 12.3, 12.4, 12.6).

Overall we found that Prof. T. Schlick's book is very adequate for a broad spectrum of levels and very accessible to both graduate and undergraduate students interested in mathematical modeling and computational biology. It is also very well organized facilitating the option of selecting parts of the material for the classroom or for use in one's research.

Beautifully written!.......2003-08-11

As a person with no expertise in molecular modeling who wanted to learn about this field, this book is right on! I find the author's lively text to be as well-written and clear as any science textbook I have ever read. I especially enjoyed the first two chapters for their historical perspective and their practical illustrations of applications of genetics in the world today. These chapters can be understood by non-scientists, whereas the rest of the book is clearly designed for graduate students in any one of the science disciplines.

The interesting information sprinkled throughout the book, including the boxes and figures, help keep the reader stimulated and yearning for greater knowledge of this exciting field. The color graphics also complement the book nicely. Although the subject covered in the book is extremely broad, the author managed to convey the perspectives of multiple scientific disciplines (e.g., biology, chemistry, computer science, math) very well. The combination of breadth and depth in a readable style is remarkable.

Overall, I highly recommend this book to readers interested in the area.

Never short of something exciting.......2003-08-11

It contains many many exiting problems and stimulate to go through the world of art of biology and mathematics. This book can guide the way where to go when you lost in the world of biomathematics, specifically molecular modeling. It looks like a well-organized dictionary with analysis. So it is good for a beginner and also deep-thinker in computational biology field.

Excellent book for both students and researchers.......2003-08-08

This book covers state-of-arts technologies used in today's hot field: molecular modeling. The book is easy to understand and it is suitable for graduate students in both theoretic majors and experimental majors. The homeworks in the book are practical for students to learn basic molecular modeling computer skills very fast.

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.

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.

Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.

The Statistics of Gene Mapping (STATISTICS FOR BIOLOGY AND HEALTH)
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The Statistics of Gene Mapping (STATISTICS FOR BIOLOGY AND HEALTH)
David Siegmund
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 038749684X

Book Description

Gene mapping is used in experimental genetics to improve the hardiness or productivity of animals or plants of agricultural value, to explore basic mechanisms of inheritance, or to study animal models of human inheritance. In human populations it is used as a first step to identify genes associated with human health and disease. This book presents a unified discussion of the statistical concepts applied in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. The development involves elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. The viewpoint reflects the modern approach of using anonymous DNA markers distributed throughout the genome to identify regions likely to contain genes of interest. The reader is assumed to have some familiarity with probability/statistics and with elementary genetics. Important topics are reviewed in the first three chapters. The R programming language is developed in the text. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The book is suitable for upper level undergraduate students or graduate students of genetics or statistics.

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

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the worst book I have ever read
Could have been a great one.
An excellent book.
A very bad book. A colection of references w/o explanations

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)
Pierre Baldi
Manufacturer: M.I.T. PRESS
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Binding: Hardcover

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ASIN: 026202506X

Book Description

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.

In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.

This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Customer Reviews:

Terrible.......2006-03-16

I'm a graduate student, reading a lot of bioinformatics materials. This is by far the worst text I've read on the subject. Poorly explained, poorly edited. Poor.

the worst book I have ever read.......2005-11-06

Just a collection of formulae, in an unclear way. Once we tried to use it in our seminar of bioinformatics, but after a few chapters we had to give it up for its bad writing. I could not find any reason to buy it or read it.

Could have been a great one........2003-12-14

This book is decidedly a mix: some very good information, combined with some very puzzling omissions and uneven editing.

First, the good. The description of stochastic context free grammars is the best I've seen. I don't know any other reference that even hint at how to use generative grammars to evaluate likelihoods. Once they caught my interest, though, the authors did not carry through with training and evaluation algorithms I could really use. I suspect that parts of the information are there, but I'll have to go back over their opaque notation again to work out just what they've given and just what's been left out.

This same pattern - an interesting introduction with missing or mysterious development - recurs throughout the book. The discussion on clustering and phylogeny goes the same way: a number of techniques are mentioned but not developed. The authors mention a tree drawing problem, not just building the tree's topology, but ordering the branches for the most informative rendering. Again, a critical topic and one that most authors miss - in the end, these authors miss it, too, by mentioning but not filling in the idea.

Their discussion of neural nets suffers badly from the authors' partial presentation. Evaluation of network output for a given input is relatively straightforward, and they present it in some detail. Training the net is the real problem, though, and is given less than a page.

Baldi and Brunak give more of the fundamentals than most authors. For example, they explain the maximum entropy principle well enough that I'll use it in lots of other areas. They give some coverage to topics of intermediate complexity, such as the forward and backward algorithms for HMM training. Finally, they fizzle out at the higher levels of complexity - the Baum-Welch algorithm could have followed from the forward and backward methods, but is left as a reference to another book.

There is some good here, especially in the fundamentals behind important techniques. The discussions I wanted - the more avanced topics, in forms I can use - are often weak, missing, or impenetrable. Just a bit more work, clearly within the authors' capability, would have made this a landmark reference.

An excellent book........2001-10-23

Very well written, clear, and self-contained. The authors provide a masterly treatment of machine learning methods (neural networks, hidden markov models, etc.) and their applications to fundamental problems in sequence analyis and biology. The book goes all the way from first principles to advanced research topics and should be valuable for both students and researchers. Second edition has many new topics, including DNA microarrays. Requires some concentration but mathematical details are summarized in the appendices. I strongly recommend it for anyone with an interest in bioinformatics and/or machine learning.

A very bad book. A colection of references w/o explanations.......2001-09-19

I just bought this book and am COMPLETEly disappointed with it.
Here is why. The book is badly written, hard to read and follow. Although it is said that this is a book is for " many readers", it is really for those who have already known all the algorithms. It is simply impossible to learn the algorithms from this book. The chapter on neural network is a few pages. It provieds a few equations for backpropagation. That is it! It is pretty much true for every thing else. Equations, hard to understand sentences, abbreviations with no explnantions, tons of citations everywhere. A book should strive to explain, and not to cite what other papers and go look there all the time. I suspect the few good reviews here are from the authors themselves.

Book Description

The advent of ever more sophisticated molecular manipulation techniques has made it clear that cellular systems are far more complex and dynamic than previously thought. At the same time, experimental techniques are providing an almost overwhelming amount of new data. It is increasingly apparent that linking molecular and cellular structure to function will require the use of new computational tools.

This book provides specific examples, across a wide range of molecular and cellular systems, of how modeling techniques can be used to explore functionally relevant molecular and cellular relationships. The modeling techniques covered are applicable to cell, developmental, structural, and mathematical biology; genetics; and computational neuroscience. The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene products. Modeling examples are provided at several scales for each subject. Each chapter includes an overview of the biological system in question and extensive references to important work in the area.

Customer Reviews:

Informative, but not information I can use.......2004-05-03

Regulatory networks are central to every aspect of computational biology. Determining what they are, and what genes, proteins, and post-translational modifications interact is a major and exciting field of study.

I just didn't come away from this book with that excitement. I was hoping for more about the large-scale regulation networks, but these papers go down to the quantum mechanics of interactions between pairs of molecules. I appreciate that the exact interactions matter, and that computation is probably the only way to examine some kinds of interactions (e.g. the ones in lethal mutations). It's just not what I think of as a "network."

I was also hoping for some more specifics about the computation techniques. There were some interesting insights here. For example, I never thought about the similarities between steady state chemical equilibrium and steady state Markov model behavior before, but the formalisms have striking similarities. I was also interested in some of the information-based measures for determining how well a model represents a system. I learned that the statistical assumptions behind normal chemical "equilibrium" break down at the scale of bacteria - instead, presence or absence of individual molecules matters more. Still, those were isolated kinds of facts and never came together into a whole for me.

The range of views was worthwhile. On the whole, though, the models all seemed very low-level to me, probably not well suited to handling more than a few dozen interactions, and the computation specifics were not always explicit. I'm still looking for a book with more information that I can apply directly.

Excellent survey of the field.......2001-08-04

An excellent survey for anyone contemplating doing research in this area. The authors make a special effort to identify the open research problems, what has been done to date and what there is very little of. This book will bridge the gap for anyone with a background in Molecular Biology that wants to build computer models for cellular and genetic activities. It is especially focused on gene regulation, but also covers other modeling areas such as diffusion. In reading this book, you will appreciatge both the good start this field is off to, but also the long way to go before a complete cell can be modeled. A great area to do pioneering work.

it's about time!!!.......2001-04-03

For many years, biologists have been accumulating descriptions of biological "parts" with an almost complete lack of a framework for understanding how those parts might really work together. This book represents the first and so far only example I have seen of an effort to describe modeling techniques that are right now being developed to construct such a framework. There are other books on "computational biology", but most of them are focused only on measuring and comparing different strands of molecules -- this book describes how computational techniques are starting to be applied to actually trying to understand how those molecules work together to generate life. On the outside jacket of the book, Bruce Alberts, President of the National Academy of Science, AND the guy whose book on molecular biology I had to buy for a lot of money when I was in college, describes the authors of this book as being "Brave". I would say it is an introduction to a "Brave New World". This has to be where biology is going -- Each of the chapters are written by different people, and as such there is some variation in readability. I also wish that the color illustrations were part of the chapter they refer to instead of being grouped in the middle. But most of the chapters start with enough of an overview to be understandable to anyone with a decent background in biology. And WOW -- biology is going to get much more exciting!! Oh one other thing -- the art on the inside of the jacket is wonderful - especially in contrast to the black cover with its standard diagram of metabolism -- I wonder if there is a message there :-) .

Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)

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interesting lateral applications

Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Brabazon A.
Manufacturer: Springer
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Binding: Hardcover

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Similar Items:

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Investment Strategies (Wiley Finance)
Introduction to Evolutionary Computing (Natural Computing Series)
Fundamentals of Computational Swarm Intelligence

Accessories:

Computation Engineering: Applied Automata Theory and Logic
Theory of Computation (Texts in Computer Science)
Linear Genetic Programming (Genetic and Evolutionary Computation)

ASIN: 3540262520

Book Description

Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies â€“ neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.

Customer Reviews:

interesting lateral applications.......2007-01-24

In the ceaseless search for better modelling of financial instruments and economic events, one approach is to look for methods from mathematical biology as inspiration. Here, the main approaches studied include neural networks, genetic algorithms and ant colony modelling. The first two are perhaps the most widely used.

The key inspiration is to look into the future. The later sections of the book involve predicting various events, like a corporate failure. The efficacy of the biological methods for doing predictions is unclear. The book's results are intriguing, though.

There appear to be 2 audiences for the book. One is biologists or programmers already using those methods in biology, and who are looking at applying these to finance. The other audience is mathematicians in finance wanting more tools. Consequently, each audience will find different portions of the book useful. The explanation of conventional financial modelling is for the biologist, for example.

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)

Average customer rating:

Very nice book, but not really for beginners
"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)
Richard C Deonier
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Statistical Methods in Bioinformatics: An Introduction (STATISTICS FOR BIOLOGY AND HEALTH)
Introduction To Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics)
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
An Introduction to Bioinformatics Algorithims (COMPUTATIONAL MOLECULAR BIOLOGY)
Essential Bioinformatics

Accessories:

Monte Carlo Statistical Methods (Springer Texts in Statistics)
The Elements of Statistical Learning
All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

ASIN: 0387987851

Book Description

Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.

This book features:

Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation

Presentation of fundamentals of probability, statistics, and algorithms

Implementation of computational methods with numerous examples based upon the R statistics package

Extensive descriptions and explanations to complement the analytical development

More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature

Exercises at the end of chapters

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Simon TavarÃ© holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor TavarÃ©'s research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. TavarÃ© is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.

Customer Reviews:

Very nice book, but not really for beginners.......2007-10-17

This textbook is used as the main text for one of my graduate courses. It is a well written book and contains a plethora of information. The problem is that I find myself constantly re-reading sections and walking through examples to thoroughly understand them. Nothing seems to click the first time I read through the information (or sometimes even second, third, etc.).

This is my first time taking any coursework in the bioinformatics field so perhaps it is simply because this material is new to me, but I found this book fairly difficult to read. I had to supplement it with other books, wikipedia entries, etc. to be able to understand many of the terms (which this book fails to define).

If you're willing to put forth the effort of filling in the gaps, then this is a great book. If you already have a strong background in computer science and biology then this is likely an excellent book for reference material, or to expand you knowledge in an already familiar area.

Also note that there is a large amount of discussion of probability in this area of study. You may wish to brush up on your skills in probability prior to reading this.

"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ.......2006-07-08

This textbook was based on the authors' instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master's, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry.

This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or 'machine' utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species.

The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computational biology to fully understand them, the authors include examples of computations using R. This volume is described as a "roll up your sleeves and get dirty" introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for intellectual growth as new experimental approaches lead to new computational tools.

One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics --- the 98% being processed in R language. It isn't intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher's answer book had been appended.

The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data.

Leonard C. Silvern
Systems Engineering Laboratories
Clarkdale, AZ

Computational Molecular Biology (Theoretical and Computational Chemistry)

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Computational Molecular Biology (Theoretical and Computational Chemistry)

Manufacturer: Elsevier Science
ProductGroup: Book
Binding: Hardcover

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ASIN: 0444500308

Book Description

This book covers applications of computational techniques to biological problems. These techniques are based by an ever-growing number of researchers with different scientific backgrounds - biologists, chemists, and physicists.

The rapid development of molecular biology in recent years has been mirrored by the rapid development of computer hardware and software. This has resulted in the development of sophisticated computational techniques and a wide range of computer simulations involving such methods. Among the areas where progress has been profound is in the modeling of DNA structure and function, the understanding at a molecular level of the role of solvents in biological phenomena, the calculation of the properties of molecular associations in aqueous solutions, computationally assisted drug design, the prediction of protein structure, and protein - DNA recognition, to mention just a few examples. This volume comprises a balanced blend of contributions covering such topics. They reveal the details of computational approaches designed for biomoleucles and provide extensive illustrations of current applications of modern techniques.

Book Description

Computational Neurogenetic Modeling is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.

High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery (Methods in Molecular Biology (Cloth))

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High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery (Methods in Molecular Biology (Cloth))
Lansing D. Taylor
Manufacturer: Humana Press
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Binding: Hardcover

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Similar Items:

High-Throughput Screening in Drug Discovery (Methods and Principles in Medicinal Chemistry)
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ASIN: 1588297314

Book Description

High content screening (HCS) was developed by Cellomics Inc. in the mid-1990s to address the need for a platform that could be used in the discovery-driven research and development required to understand the functions of genes and gene products at the level of the cell. High Content Screening: A Powerful Approach to Systems Cell Biology and Drug Discovery discusses its use as a high throughput platform to understand the functions of genes, RNA, proteins, and other cellular constituents at the level of the living cell.
High Content Screening is assembled to assist both existing users of HCS, as well as investigators considering the addition of a discovery-driven platform to their research and development activities. The chapters have been organized into sections that highlight the importance of integrating instrumentation, application software, reagents, and informatics. In addition, there is a combination of pure review chapters on key topics and specific methods chapters.
Written by a team of world leaders in the field, High Content Screening is essential reading for senior scientists, advanced lab technicians, as well as students/post-docs who wish to apply HCS in their research and development programs.

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