Book Description
This mainstream, full-color physical anthropology text is the best-selling text in the market! While it continues to present a comprehensive, well-balanced introduction to the field of physical anthropology, this is a major revision and the book has shifted emphases in critical areas of biology, including molecular biology and genetics, to reflect the field as it stands today. Now, as a Media Edition, INTRODUCTION TO PHYSICAL ANTHROPOLOGY automatically comes with the new BASIC GENETICS CD which responds to growing interest in genetic variation driven by advances in molecular biology enhance.
Customer Reviews:
Excellent, brand new and shipped fast.......2007-02-21
This was a good deal, it was brand new and it shipped very fast. I was impressed.
Amazon.com
Inheriting the mantle of revolutionary biologist from Darwin, Watson, and Crick, Richard Dawkins forced an enormous change in the way we see ourselves and the world with the publication of The Selfish Gene. Suppose, instead of thinking about organisms using genes to reproduce themselves, as we had since Mendel's work was rediscovered, we turn it around and imagine that "our" genes build and maintain us in order to make more genes. That simple reversal seems to answer many puzzlers which had stumped scientists for years, and we haven't thought of evolution in the same way since.
Why are there miles and miles of "unused" DNA within each of our bodies? Why should a bee give up its own chance to reproduce to help raise her sisters and brothers? With a prophet's clarity, Dawkins told us the answers from the perspective of molecules competing for limited space and resources to produce more of their own kind. Drawing fascinating examples from every field of biology, he paved the way for a serious re-evaluation of evolution. He also introduced the concept of self-reproducing ideas, or memes, which (seemingly) use humans exclusively for their propagation. If we are puppets, he says, at least we can try to understand our strings. --Rob Lightner
Book Description
The million copy international bestseller, critically acclaimed and translated into over 25 languages. This 30th anniversary edition includes a new introduction from the author as well as the original prefaces and foreword, and extracts from early reviews. As relevant and influential today as when it was first published, The Selfish Gene has become a classic exposition of evolutionary thought. Professor Dawkins articulates a gene's eye view of evolution - a view giving centre stage to these persistent units of information, and in which organisms can be seen as vehicles for their replication. This imaginative, powerful, and stylistically brilliant work not only brought the insights of Neo-Darwinism to a wide audience, but galvanized the biology community, generating much debate and stimulating whole new areas of research.
Customer Reviews:
Excellent overview of evolution.......2007-10-08
Scholars pro-evolution can generally be divided into 1) those who believe in evolution at the group level (ie: The reason lions behave in a particular way is because they want to survive as a species) or 2) those who believe in evolution at an individual level (ie: The reason a particular bird behaves in a particular way is because he wants to survive as an individual bird). Dawkins' views are closer to the latter. In fact, he takes it a step further and argues for evolution at the gene level. I think he makes a very convincing case for his views. Of course nothing is certain (except uncertainty perhaps) so he does not prove his theory definitively.
The book can be hard to read at times and may be a bit slow for those with no background in biology or science. Nevertheless I think anyone with patience can read, enjoy and learn from this important book.
No matter what your views this is a very educational and important book. Highly recommended.
Fancifully Dark.......2007-09-21
In his play "Suddenly, Last Summer," Tennessee Williams writes of a young man who, on vacation at the seashore, watches newly hatched baby turtles struggling down to the safety of the water. Only a small percentage get there, though, because the gulls overhead scoop them up and eat them faster than they can crawl. The young man, observing this and already under great psychic tension, tells his cousin that now "I have seen God!" Later on, we realize the man is morally insane, and that perhaps this was the turning point; his descent into insanity.
Whether Williams himself thought of God the same way, I don't know. But certainly the example of the turtles and gulls had been chosen, out of thousands of other such biological observations, because the young man chose to find God - or truth - in it. He could, if he had been in a sunnier mood, chosen to look at nesting robins or a mare and her colt.
In "The Selfish Gene," Dr. Dawkins argues against the idea of altruism in nature: mothers take care of their young because they love them, etc. Dawkins says they do it because it's in their genes. But he takes it one step further: he says it's the genes themselves that are struggling to survive - not the whole animal. The analogy of genes "using" animal bodies for their own "selfish" ends, as if we were robots and the genes our drivers, is made over and over again.
Of course, Dawkins realizes this is not correct. Darwinism asserts that biological life came into existence blindly: cells and animals came (and continue to come) into being, not because they chose to, but because of natural selection. And the ones who survive do so because of serendipity.
This is a very hard concept, of course, to understand. I remember Sister Pauline laboring to explain to us girls in junior biology class that the white butterflies didn't decide to turn black; they turned black over generations, due to natural selection. She had a hard time of it. (Catholics are "allowed" to choose between a literal or analogous interpretation of the Bible, so she was not breaking any official rules!)
In other words, a "selfish" gene (or animal) makes no more sense in Darwinistic terms than an "altruistic" one.
Dawkins explicitly states this on page 196. But he uses the selfish gene analogy so many more times - hundreds of times - that, just from the sheer repetitiveness of the theme, it may sink in too deeply (and do some psychic damage) to people who are not currently living on the sunny side of the street, so to speak.
For those people, like the gentleman who wrote the touching review of how this book contributed to fits of depression, I'd say: This point of view has no more legitimacy than the altrustic point of view.
An additional (and, I think, unnecessary) weight on the sensitive reader's soul is the aspersions that Dr. Dawkins, an aggressive atheist, throws on the concept of God - limited mainly, I think, to his chapter on memes (he saves most of his vituperation on this issue for another book, "The God Delusion").
On that score, I'd say: please realize that scientists don't know everything. People in different professions develop different mental prejudices: lawyers think like lawyers, engineers like engineers, etc. And scientists, for whom scientific method is everything, tend to think that anything that's not measurable therefore doesn't exist. This is a logical fallacy. They also tend to think they are so intelligent, and the world outside science is so simple, that they can read a few survey books on religion, philosophy, or history and know all there is to know about the field. This leads them to made irresponsible, blanket statements, completely unaware of how little they know.
On page 201, he winds up a chapter by saying all is not gloomy; humans can still strive towards altruism; that "[w]e, alone on earth, can rebel against the tyranny of the selfish replicators." Then, in a long footnote, he writes that some of his colleagues disapproved of this passionate summation. "In some cases, the criticism came from doctrinaire sociobiologists jealously protective of genetic influence...." and in others, from "high priests of the left jealously protective of a favorite demonological icon!" (His exclamation point.) These latter, apparently, were objecting that he on the one hand implied a belief in free will while on the other hand talking like a genetic determinist. He objects to this, saying, if I understand it, that he's both, and ends the argument by saying, "We, that is our brains, are separate and independent enough from our genes to rebel against them. ...[W]e do so in a small way every time we use contraception."
Now, I don't pretend to have a handle on the philosophical and sociobiological arguments regarding whether or not humans have free will, or even what exactly free will is. But in the above I don't see that Dr. Dawkins really does, either: he treats it far too simply.
In sum, read the book, but don't let it get you down. After all, if the village priest doesn't have the right to bully people intellectually, than neither does the research scientist.
Mandatory reading for students/interested persons.......2007-09-17
This is an excellent primer to biological evolution and could also be a valuable co-text with a standard high school biology course. Written in British english, it is quite understandable though more academic than casual.
Dawkins' use of the 'gene's eye view' of the world permeates the text. It is very easy to follow. A great book to rebut any creationist's
viewpoint. This was Dawkins' first book in the field. It will not disappoint or talk down to you.
Enjoy.
a mixed bag.......2007-09-09
Parts of the book were utterly fascinating to me, such as the groundbreaking idea of the "meme" as a unit of cultural transmission. But the argument that species are survival machines for our "immortal genes" seems seriously flawed.
First and foremost in my mind, it is hard for me to swallow that organisms practice altruism because the gene or genes that are responsible for this altruism have a probability of existing in the recipient of the altruism, the probability increasing with the closeness of familial relatedness. How, then, to explain altruism beyond the family, or even beyond the species? The author mentions that there is at least one well-documented case of a dolphin rescuing a drowning human being. In the book this was suggested to be a mistake. One of Earth's most intelligent animals has a gene for rescuing long, narrow objects and cannot tell the difference between a human and its own species. I got doubtful when contradictory evidence was explained as a mistake. And what about organisms' adopting children originating from other parents? Always a mistake? Highly expensive practice for when the real deal arises?
I do not understand why there must be *a* unit of natural selection. Can't there be more than one, sometimes at odds with each other, sometimes in tandem? I do believe genes are selected over other genes, but I believe groups can be selected over other groups, too. Perhaps other units, both larger and smaller than genes (Why not the selfish base pair?), are also naturally selected.
Finally, especially considering recent discoveries in genomics that have downsized the number of estimated human genes, there cannot be one gene behind any behavioral trait you can think of, a gene for being nice to your cousin, for example. I get that a gene can have many functions and can have a net effect of being nice to your cousin, and in its absence you would be less nice to your cousin, but this makes for a complicated web which would get torn apart as succeeding generations inherit just part of the web. The influences of biology, environment, and history get harder to ignore.
a new way to look at the world.......2007-08-29
Dawkins challenges us to look at old ideas (Darwinian) in a new light. At times I found his mathematical calculations tedious to follow, however when I bothered to think them through, they did make sense. I esepcially enjoyed the chapter on game theory because it helped to explain why humans do not usually behave in blatantly exploitive ways in spite of our "selfish genes". I recommend the book to anyone who wonders how the world works.
Customer Reviews:
Must have........2006-03-18
I'm taking genetics this semester and it's gotta be one of the hardest classes I've ever taken. The professor assigns us a lot of the problems out of the book, and I don't understand how to approach them all. The solutions manual shows some really good ways to approach genetics problems and has really helped me. I recommend it, though get it used from someone else if you can to save a few bucks. If you do the reading and work the problems and check your answers with the solutions manual, you'll understand genetics a lot better.
Intro to Genetics solutions manual.......2006-03-08
A good buy--it really goes into detail with hard problems and is quite helpful to have!
Genetic Analysis Solutions Manual review.......2006-02-22
The solutions manual is excellent. The Interactive CD that comes with it is one of the best I have ever used. I wish that more interactive genetics was contained on the CD or even possibly a tutorial of the information contained within the Genetics book.
A Great Safety Net for Understanding.......2006-02-20
This study guide works through each end-of-chapter question step by step and really helps to solve many complex problems associated with genetic analysis. Also the interactive CD Rom that accompanies the solutions manual is great for visusalizing complex processes.
Genetics = Cake?.......2006-02-18
It's not like I really really wanted my textbook (nobody wants to study that desperately). Thanks guys! Now I can start procrastinating without worrying about not having the book as backup the night before the test! Haha.
Actually, the structure of this textbook is very well organized. The example problems are thorough and the explanations are equally well-thought-out. Combined with a reasonable lecture/prof, I think the text material is very simple and appropriate- just enough pictures and diagrams to get you by at 3AM after a few eye-burning games of DDR or whatever.
Book Description
An Introduction to Human Molecular Genetics
Second Edition
Jack J. Pasternak
The Second Edition of this internationally acclaimed text expands its coverage of the molecular genetics of inherited human diseases with the latest research findings and discoveries. Using a unique, systems-based approach, the text offers readers a thorough explanation of the gene discovery process and how defective genes are linked to inherited disease states in major organ and tissue systems. All the latest developments in functional genomics, proteomics, and microarray technology have been thoroughly incorporated into the text.
The first part of the text introduces readers to the fundamentals of cytogenetics and Mendelian genetics. Next, techniques and strategies for gene manipulation, mapping, and isolation are examined. Readers will particularly appreciate the text's exceptionally thorough and clear explanation of genetic mapping. The final part features unique coverage of the molecular genetics of distinct biological systems, covering muscle, neurological, eye, cancer, and mitochondrial disorders. Throughout the text, helpful figures and diagrams illustrate and clarify complex material.
Readers familiar with the first edition will recognize the text's same lucid and engaging style, and will find a wealth of new and expanded material that brings them fully up to date with a current understanding of the field, including:
* New chapters on complex genetic disorders, genomic imprinting, and human population genetics
* Expanded and fully revised section on clinical genetics, covering diagnostic testing, molecular screening, and various treatments
This text is targeted at upper-level undergraduate students, graduate students, and medical students. It is also an excellent reference for researchers and physicians who need a clinically relevant reference for the molecular genetics of inherited human diseases.
Book Description
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.
This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.
The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.
The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.
Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.
Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.
Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).
Customer Reviews:
Lots of material made accessible.......2007-10-10
I'm a Statistics PhD student so you can condition on my prior to get at what's really going on with this book.
Bioinformatics is a departure from "regular" statistics and looks awfully messy at first pass. The sorts of assumptions one typically makes in other areas of statistical inference are patently false, so new techniques and intuitions have to be built up in order to attack these kinds of problems. This book does an excellent job of balancing the technical details with the necessary intuitions so one can really get a firm grasp on what's going on.
I wouldn't recommend this book to someone who hasn't done statistics at at least an advanced undergrad level (e.g., comfortable with Probability at the Ross-level and Statistical Inference at the Casella/Berger-level). But for people really interested in the material and coming from a solid statistical background the book is an excellent resource.
I would also strongly recommend it to teach out of.
Most Elegant Account of Bioinformatics.......2004-11-27
I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes. But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).
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.
Customer Reviews:
Not my favorite book!.......2007-08-07
I found this book to be confusing and hard to decipher at times!! I sometimes had to read the paragraph several times to get it and then I would find later in the book somewhere, what I was confused about was explained somewhat better. This book caused me to waste a lot of time trying figure out things. Perhaps better organization would be helpful. I wouldn't recomend this book.
very introductory.......2007-05-29
Covers the topics very basically. Good for an undergrad class but not anything more.
an introduction to gentics Analyis.......2007-04-14
The first time we recieved this book it was the answers to text questions only. The second time we did not order it but somehow by just looking at the web site it got orderd thus we had to return it cause we had already purchased it somewhere else.
Good.......2005-09-22
It was just as described, and I am happy with the item I got. Thanks a lot!
Ok Genetics Book.......2005-09-06
The questions in the back of each chapter are really helpful. Sometimes the chapter itself is a bit vague. The chapter describing bacteria genetics was done very poorly. Even my TA agreed that the way it's written is hard for beginners to understand.
Book Description
Known world-wide as the standard introductory text to this important and exciting area, the fifth edition of Gene Cloning and DNA Analysis addresses new and growing areas of research whilst retaining the philosophy of the previous editions. Assuming the reader has little prior knowledge of the subject, its importance, the principles of the techniques used and their applications are all carefully laid out, with over 250 clearly presented two-colour illustrations.In addition to a number of informative changes to the text throughout the book, the final four chapters have been significantly updated and extended to reflect the striking advances made in recent years in the applications of gene cloning and DNA analysis in biotechnology.
Customer Reviews:
clearly written undergraduate text.......2006-11-30
As an introductory text on its subject, the book is well written. With copious diagrams that are easy to understand and that illustrate key ideas. A merit of the book is the clarity of the textual exposition, reinforced by those diagrams.
The text is also quite up to date in this fast changing field. With the good coverage of many topics. Including the seminal Polymerase Chain Reaction, that is the basis of so much else. You can see that genomics/biotechnology is now a practical and quantitative science. With plenty remaining to be understood, to be sure. But the book shows that we now have powerful tools to experiment with, to reduce our ignorance.
Up to date and still very readable.......2006-04-12
This book has become the standard introductory text at the undergraduate level for students in the first or second year of college and as an introductory book for researchers whose specialty lies in other areas but needing to know more about the subject. While an introductory text, it does presume that you are approaching the subject with at least some background in biology. If nothing else, you need to know what a gene is and have some idea about why you would want to clone it.
This basic book has been around for about twenty years. The twenty years since then have seen tremendous advances in the techniques and science as they now exist. This is the fifth edition of the book and it is as up to date as any printed book can be.
Since the book was written the public awareness of genetically altered plants has increased tremendously. A major goal of this new edition is to present to the student the true facts about genetically modified agricultural products. The final chapter on Forensic science and Archaeology is most fascinating as it provides a non technical look at DNA analysis in criminal acts and in the tracing of the human species.
Highly recommended.
A Good Book for Introductory Courses in Molecular Biology.......2004-12-31
I am a science student Studying Biochemistry and Molecular Biology. I used this book as a reference book for an Introduction to Genetic Engineering course. It was a great help for me. The book outlines the basic principles and methods in many aspects of Molecular Biology and Genetic Engineering in the simplest of ways. The book is easy to understand even to people with no big background in Molecular Biology as it explains everything from zero. It is a great book for introductory courses or as a quick reference for the basic ideas of some techniques and advances in the field. It does not give a lot of detail and explanation that is usually required from university students, such as myself, so more in-depth references are certainley required. However, as a joyful read for those interested in the field, or simply as a quick revision of the basics before your final exam, this book works wonders! Trust me... I got a straight A (99% on my final) :) All in all, it's a good book!
A Versatile, Accessible Introduction.......2001-10-14
I came away very impressed from Dr. Brown's latest edition. The book is extremely readable but does not dumb down the material. I'm taking an upper-level molecular genetics lab and am doing independent work in genetics, and this book is a great reference. However, I think that this book would be pretty easy for someone with introductory level biology--heck, I think some AP Biology high school teachers may be able to use this text for their classes.
Brown takes you through all the basics of molecular genetics: from the basic mechanics of DNA manipulation to PCR, bacteriophages, and even a review of basic genomics and genomic analysis, which are still very new and rapidly evolving fields. Every chapter has references for more in-depth study. This is a great book to introduce you to modern molecular genetics.
Book Description
What is life? Has molecular biology given us a satisfactory answer to this question? And if not, why, and how to carry on from there? This book examines life not from the reductionist point of view, but rather asks the question: what are the universal properties of living systems and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation has been deliberately kept fairly non-technical so as to address a broad spectrum of students and researchers from the natural sciences and informatics.
Book Description
Significant advances in DNA analysis techniques have surfaced since the 1997 publication of the bestselling An Introduction to Forensic DNA Analysis. DNA typing has become increasingly automated and miniaturized. Also, with the advent of Short Tandem Repeat (STR) technology, even the most minute sample of degraded DNA can yield a profile, providing valuable case information. However, just as the judicial system slowly and reluctantly accepted RFLP and AmpliType® PM+DQA1 typing, it is now scrutinizing the admissibility of STRs. Acknowledging STR typing as the current system of choice, An Introduction to Forensic DNA Analysis, Second Edition translates new and established concepts into plain English so that laypeople can gain insight into how DNA analysis works, from sample collection to interpretation of results. In response to the shift toward more efficient techniques, the authors cover the legal admissibility of STR typing, expand the chapter on DNA databases, and revise the section on automated analysis. They also present key decisions and appellate or supreme court rulings that provide precedent at the state and federal levels. Discussing forensic DNA issues from both a scientific and a legal perspective, the authors of An Introduction to Forensic DNA Analysis, Second Edition present the material in a manner understandable by professionals in the legal system, law enforcement, and forensic science. They cover general principles in a clear fashion and include a glossary of terms and other useful appendices for easy reference.
Customer Reviews:
Good textbook for intro class.......2006-03-11
I suppose the most important question is why are you interested in this book. It would make a good textbook for an intro type class in this area or would be good for someone who doesn't know DNA analysis, but is involved in criminalistics. The level of detail is insufficient for anyone who actually understands the molecular approaches. It spends a fair bit of time talking about cases and HOW this particular approach was once useful. These are interesting little stories, but the book really didn't give me what I wanted. I have a background in molecular genetics and was interested in making a career shift to DNA forensics. There was little of value for me because it never really got to the details. I think it would be good for a class because it provides historical perspective on the now outdated techniques that would be important background for someone who never knew anything about the development of the previous techniques and only learned what is current today, but to someone who has that background...seemed like it just added a new chapter to an otherwise outdated book.
A great intro for the beginner.......2002-06-11
Having left behind my interest in genetics in order to pursue tax accountancy, I've always been fascinated by accounts of DNA typing. This book provides a wonderful introduction that is accessible to even the novice geneticist like myself. Much of the information Rudin and Inman give is quite practical; with some help from a friend who works in a medical lab I was able to set up my own electrophoresis gels and PCR. A word of caution to the amateur, however: make sure you practice before drawing any conclusions from the "evidence." My wife, Amy, wouldn't speak to me for a week after I claimed that a stain on our bed linen did not match my DNA. It turned out that she had spilled some ice cream.
vivid introduction to forensic genotyping techniques.......2001-02-08
Nice figures, photos, schemes, and U.S. tables; lively comparisons show that authors are good teachers. Though technical part of the book outdates fast necessarily (acronym SNP has not been coined in 1996 yet), it is a shame that an interpretation part of the book neglects the current knowledge. Bayes theorem is known since 1763 and, nevertheless, likelihood ratio is not even mentined in the book! Books of Evett and Weir or of Robertson and Vignaux cannot be substituted by this book.
A must-have for any DNA criminalist or criminal lawyer.......2000-07-06
I have a BS in genetics and biochemistry and am looking to enter into a forensics lab. This book is an EXCELLENT resource for an entry-level criminalist, criminal lawyer, or the non-scientist interested in this topic. It was organized from basic genetics to higher-level interpretation issues and included tons of diagrams, pictures, and relevant case studies. This book did an outstanding job explaining complicated and detailed subject matter in an easy to understand and interesting matter.
Attorney's Guide.......2000-02-08
As a practicing lawyer who earns a living in the criminal courts, I found this book an excellent and informative guide to this often problematic area. The authors introduce the subject in a way that is easy for the beginner to comprehend, but at the same time include sufficient detail to answer many of the most pressing problems facing a defence team in court.
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