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 Schlick¿s ¿Molecular Modeling and Simulation: An Interdisciplinary Guide¿:||"The interdisciplinary structural biology community has waited long for a book of this kind which provides an excellent introduction to molecular modeling.¿|¿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.¿|¿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.¿|¿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

Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.

Customer Reviews:

Great reference.......2007-09-06

A great reference and a good introduction to many important concepts in sequence analysis. However, if you don't have a reasonable grounding in math you may struggle with the terse notation.

Borodovsky's companion book is an excellent partner for this book. Get both.

One of the best available.......2007-08-17

Although this book is based primarily on work that was completed in 1998, and therefore somewhat out of date, it is the best book I have found for teaching bioinformatics. I selected this as the best of the available books on the subject for use in my bioinformatics and numerical methods course which is to be taught in the fall of 2007 at Univ. of Conn. This course is an upper division undergraduate and first year graduate course. That is roughly the level of this text and the comparative advantage of this book is the excellent presentation and thorough discussion of the algorithms. A student armed with Matlab or MathScriptor can take this book and start writing algorithms for sequence alignment and Hidden Markov Method (HMM) analysis after only the first three or four chapters. This book is in its 11th printing and is nearly error free (I found only a few in the figures). This book is strongly recommended for both students and researchers, particularly those interested in protein alignment, phylogenic analysis or an introduction to Hidden Markov Methods.

Biological Sequence Analysis.......2006-03-07

This is a very good book. I got it for a class and it is very helpful and insightful.

Truly an Excellent Book.......2006-02-18

I will agree and submit: this is an invaluable introduction to the field of bioinformatics. With introductions to everything from sequence analysis to hidden markov models and even a primer on grammars, this is a useful introduction both to biological applications for computer scientists *as well as* computational methods for biologists.

I am in a joint graduate-level biology/computer science class and we are using this book as a foundation to bring both groups up to speed and it seems to be working out nicely.

However, one criticism is that sometimes Durbin et al jump into subjects without an adequate introduction or with one that is overcomplexified. In other words, they sometimes break Einstein's the rule of "make everything as simple as possible but not simpler". Durbin et al do not always make things as simple as possible. And it is annoying when they do not. Especially when I see them confusing the bejebus out of the biology people over computer science concepts that are really not that complicated through overly technical jargon.

But this is rare and they provide many insightful diagrams to clear up their algorithms as well as lucid ways to introduce biological concepts. Sometimes the introduction of an algorithm/theory *and* a biological concept molds together beautifully such that the reader is simultaneously being infused with both. An example of this phenomenon is their dual introduction to CpG islands and markov models.

Excellent book ... a little boring to read ..........2005-09-30

I bought "Biological Sequence Analysis" for my introductory bioinformatics course. AS the course covers almost everything mentioned in the book I have (almost) finished reading and studying it.

I find this book an excellent textbook but wouldn't consider it a classic. There are some important topics missing or some topics are just briefly touched upon. (e.g. heuristic pairwaise alignment) Maybe it's just because of my theoretical background, but I find that the book does a poor job in explaining/proving the intuition behind certain aspects of the algorithms (e.d. why does a convex gap penalty lead to a different complexity than a strictly increasing gap penalty ...) . On the other hand, the probabilistic foundations of the different techniques is well written.

My final remark is that the book is not fun to read at all. The authors have made no effort to spice up the content with some historical background, some explanations of how the theory fits in the bigger picture ...

Summarized: an excellent textbook for anyone taking a course in bioinformatics but do not use this book to wet your appetite for the field ...

Average customer rating:

Model Organisms in Drug Discovery

Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0470848936

Book Description

Fruit flies are "little people with wings" goes the saying in the scientific community, ever since the completion of the Human Genome Project and its revelations about the similarity amongst the genomes of different organisms. It is humbling that most signalling pathways which "define" humans are conserved in Drosophila, the common fruit fly.

Feed a fruit fly caffeine and it has trouble falling asleep; feed it antihistamines and it cannot stay awake. A C. elegans worm placed on the antidepressant flouxetine has increased serotonin levels in its tiny brain. Yeast treated with chemotherapeutics stop their cell division. Removal of a single gene from a mouse or zebrafish can cause the animals to develop Alzheimer’s disease or heart disease. These organisms are utilized as surrogates to investigate the function and design of complex human biological systems.

Advances in bioinformatics, proteomics, automation technologies and their application to model organism systems now occur on an industrial scale. The integration of model systems into the drug discovery process, the speed of the tools, and the in vivo validation data that these models can provide, will clearly help definition of disease biology and high-quality target validation. Enhanced target selection will lead to the more efficacious and less toxic therapeutic compounds of the future.

Leading experts in the field provide detailed accounts of model organism research that have impacted on specific therapeutic areas and they examine state-of-the-art applications of model systems, describing real life applications and their possible impact in the future.

This book will be of interest to geneticists, bioinformaticians, pharmacologists, molecular biologists and people working in the pharmaceutical industry, particularly genomics.

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Customer Reviews:

Badly Needed Advice to the Young Experimentor.......2007-01-03

Students are taught to do expeiments in college, but these are carefully planned with the procedure and the results well tested. As the students move towards a graduate degree, they are expected to begin to do research of an original nature where neither the procedure nor the results are known in advance. This is a big step, and in general there has been little practical advice on how the student should proceed, just what is expected of him, how he is to work with his major professor, even how to know when he has found something.

This book, according to the author, is the result of a dinner with some fellow scientists where the wine flowed freely. They were lamenting about the differences in what students were being taught and the way that science was actually practiced.

The book lays out what happens to a real project. It begins with the first step of defining the problem to be studied, conducting research on what's been done on that problem, defining the experiments, controls, experimenting, confirming, peer review, using the results for predictions, and finally continuing the developmental effort into the future.

This is not a large book, but it provides a wealth of knowledge of the real world that the academic is preparing to enter. It is written in a light and entertaining style that doesn't hide the fact that the author has 'been there, done that.'

Highly Recommended!

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

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A modern classic
Packed full of good information

Introduction to Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics)
Michael S. Waterman
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology
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ASIN: 0412993910

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.

What's Wrong With My Mouse: Behavioral Phenotyping of Transgenic and Knockout Mice

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Valuable Book
Essential Book
Expert Review
Review
an excellent resource

What's Wrong With My Mouse: Behavioral Phenotyping of Transgenic and Knockout Mice
Jacqueline N., PhD Crawley
Manufacturer: Wiley-Liss
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471471925

Book Description

Dr. Jacqueline N. Crawley, author of the First and Second Editions of What’s Wrong with My Mouse? Behavioral Phenotyping of Transgenic and Knockout Mice,continues to field calls and e-mails from molecular geneticists who ask: how do I run behavioral assays to find out what’s wrong with my mouse?

Turn to What’s Wrong with My Mouse? to discover the wealth of mouse behavioral tasks and to get the guidance you need to select the best methods and necessary controls. Chapters are organized by behavioral domain, including measurements of general health, motor functions, sensory abilities, learning and memory, feeding and drinking, reproductive, social, emotional, and reward behaviors in mutant mice. Throughout the chapters, new behavioral tasks and new research discoveries have been added, bringing the Second Edition up to date with the latest science. In addition, the Second Edition includes two new chapters:

"Neurodevelopment and Neurodegeneration" discusses mouse behavioral tasks relevant to neurodevelopmental diseases, such as mental retardation and autism, and to neurodegenerative diseases, such as Alzheimers, Parkinsons, Huntingtons, and amyotrophic lateral sclerosis.

"Putting It All Together" recommends strategies for optimizing a battery of behavioral phenotyping tests to address your specific hypotheses about gene functions.

The final chapter, "The Next Generation," examines new and emerging technologies.

Throughout the book, the use of behavioral testing equipment is illustrated with photographs, diagrams, and representative data. Examples of behavioral tasks successfully applied to transgenic and knockout mouse models are provided, as well as references to the primary literature and step-by-step methods protocols. These features, along with a comprehensive index, listings of database and vendor websites, and an extensive list of references, make this book a valuable and practical resource for students and researchers.

Customer Reviews:

Valuable Book.......2002-11-19

"...this valuable book is currently the most complete overview of behavioral procedures available...it is a must have and a must read book..." (Genes, Brain, and Behavior, 2002)

Essential Book.......2001-07-24

"I would recommend that every behavioral scientist has at least two copies, one for their own use and one that will be on permanent loan to their students, post-doctoral students and colleagues in molecular biology." -- TRENDS in Pharmacological Sciences (Gerard R. Dawson, Merck Sharp & Dohme Research Laboratories)

Expert Review.......2001-07-24

"[T]his volume succeeds as a useful introduction to the realm of behavioral phenotyping for those interested in creating or using the large and increasing number of promising targeted mutant mice." -- TRENDS in Neurosciences (John K. Belknap, Oregon Health Sciences University)

Review.......2000-06-15

It is a much prized addition... and fulfills a heretofore unmet need for a comprehensive sourcebook of mutant mouse literature and procedures. In addition to its reference utility, Dr. Crawley's text can exert a valuable influence on the future of transgenic and knockout mouse research by standardizing behavioral phenotyping methods according to the present state-of-the-art. --Stephen C. Heinrichs, Ph.D., Boston College

an excellent resource.......2000-06-08

Very timely given the increasingly recognized importance of providing behavioral phenotypes of mutant mice. I would recommend the book with enthusiasm. --Eric Nestler, Yale University School of Medicine

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Small-Animal SPECT Imaging

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 038725143X

Book Description

Small-Animal SPECT Imaging is an edited work derived from the first workshop on Small-Animal SPECT Imaging held January 14-16, 2004 at the University of Arizona, Tucson, AZ, USA. The overall goal of the meeting and therefore this volume is to promote information exchange and collaboration between the research groups developing systems for small-animal applications. Topics include the biomedical significance of small-animal imaging, an overview of detector technologies including scintillation cameras and semi-conductor arrays, imager design and data acquisition systems, animal handling and anesthesia issues, objective assessment of image quality, and system modeling and reconstruction algorithms.

Receptors: Models for Binding, Trafficking, and Signaling

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Excellent kinetics book
A book for the modern biologist

Receptors: Models for Binding, Trafficking, and Signaling
Douglas A. Lauffenburger , and Jennifer Linderman
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Paperback

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ASIN: 0195106636

Book Description

Receptors: Models for Binding, Trafficking, and Signaling bridges the gap between chemical engineering and cell biology by lucidly and practically demonstrating how a mathematical modeling approach combined with quantitative experiments can provide enhanced understanding of cell phenomena involving receptor/ligand interactions. In stressing the need for a quantitative understanding of how receptor-mediated cell functions depend on receptor and ligand properties, the book offers comprehensive treatments of both basic and state-of-the-art model frameworks that span the entire spectrum of receptor processes--from fundamental cell surface binding, intracellular trafficking, and signal transduction events to the cell behavioral functions they govern, including proliferation, adhesion, and migration. The book emphasizes mechanistic models that are accessible to experimental testing and includes detailed examples of important contemporary issues. This much-needed book introduces chemical engineers and bioengineers to important problems in receptor biology and familiarizes cell biologists with the insights that can be gained from engineering analysis and synthesis. As such, chemical engineers, researchers, and advanced students in the fields of biotechnology, biomedical sciences, bioengineering, and molecular cell biology will find this book to be conceptually rich, timely, and useful.

Customer Reviews:

Excellent kinetics book.......2005-09-22

If you are thinking of purchasing a book on receptor-ligand kinetics, this is a wise choice. It is easy to follow and advanced enough for most graduate work in biochemical and biomedical engineering.

A book for the modern biologist.......2004-04-23

This book will surely be thought of as the first structured presentation of principles of modern systems biology. This book introduces the application of Chemical Engineering to biologists and molecular biology and pharmacology of receptors to Chemical Engineers. The top-down picture of coupled processes is not only new to biologists but introduces a whole new way to look at biological problems. Especially the chapter which has cell migration explained introduces one to multi-dimensional thinking: cell mechanics, biochemistry, kinetic modeling and molecular biology all in the same problem. This may seem mystical to many. Would probably be read more by Engineers than biologists, who are yet to take the leap in thought to multi-dimensional science.This book would be remembered after many years to be a pioneer in the new science of systems biology, although it was written much before the Human Genome Project. Systems thinking and Chemical Engineering has been much beneficial in solving some very important biomedical problems like Drug Delivery(Robert Langer), Tumor Biology(Rakesh Jain) and Tissue Engineering(Robert Langer et.al.). Lauffenburger's work would be counted among these shining lights when these methods and tools bear fruit sooner than later.(Lauffenburger has already provided a glimpse of success of this approach when he worked with Amgen to produce a much pharmacokinetically improved biologic using physiologically based pharmacokinetic model coupled with cellular modeling of receptor-ligand trafficking and binding model)Nature,5 August 2002.Lauffenburger and Linderman are geniuses seeking support for "return to science" for biology.

Disease Evolution: Models, Concepts, and Data Analyses (Dimacs Series in Discrete Mathematics and Theoretical Computer Science)

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Disease Evolution: Models, Concepts, and Data Analyses (Dimacs Series in Discrete Mathematics and Theoretical Computer Science)

Manufacturer: American Mathematical Society
ProductGroup: Book
Binding: Hardcover

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ASIN: 0821837532

Book Description

Infectious diseases are continuing to threaten humankind. While some diseases have been controlled, new diseases are constantly appearing. Others are now reappearing in forms that are resistant to drug treatments. A capacity for continual re-adaptation furnishes pathogens with the power to escape our control efforts through evolution. This makes it imperative to understand the complex selection pressures that are shaping and reshaping diseases. Modern models of evolutionary epidemiology provide powerful tools for creating, expressing, and testing such understanding.

Bringing together international leaders in the field, this volume offers a panoramic tour of topical developments in understanding the mechanisms of disease evolution. The volume's first part elucidates the general concepts underlying models of disease evolution. Methodological challenges addressed include those posed by spatial structure, stochastic dynamics, disease phases and classes, single- and multi-drug resistance, the heterogeneity of host populations and tissues, and the intricate coupling of disease evolution with between-host and within-host dynamics. The book's second part shows how these methods are utilized for investigating the dynamics and evolution of specific diseases, including HIV/AIDS, tuberculosis, SARS, malaria, and human rhinovirus infections.

This volume is particularly suited for introducing young scientists and established researchers with backgrounds in mathematics, computer science, or biology to the current techniques and challenges of mathematical evolutionary epidemiology.

Book Description

Mice have become the species of choice for modeling the complex interactions between tumor cells and the host environment. Mouse genetics are easily manipulated, and a growing array of technology exists for this purpose. Mouse models allow investigators to better understand causal relationships between specific genetic alterations and tumors, utilize new imaging techniques, and test novel therapies. Recent developments along these lines show great promise for the development of new anti-cancer treatments.

Mouse Models of Human Cancer provides researchers and students with a complete resource on the subject, systematically presenting the principles, methodologies, applications, and challenges associated with this exciting field. Offering a survey of the latest research and a description of future areas of interest, this text:

Presents real experimental data
Describes organ site-specific mouse models
Clearly identifies suitable models for further drug testing
Critically analyzes current methodologies and their limitations
Features numerous recognizable expert contributors
Lists key Web sites, reagents, and companies

From mouse handling and genetic engineering to preclinical trials, Mouse Models of Human Cancer is a comprehensive guide to using these models and relating them to human disease. Its uniform presentation describes organ-specific models in clinical, imaging, and molecular terms, and lays out the relevant genetics, experimental approaches, histological comparisons with human disease, and conclusions.

Combining stellar chapter authors, rich illustrations, and clear, up-to-date coverage, Mouse Models of Human Cancer is an invaluable resource for advanced students and cutting-edge researchers.

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