Book Description
Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own.
The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction.
Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists.
- A how-to guide for developing new mathematical models in biology
- Provides step-by-step recipes for constructing and analyzing models
- Interesting biological applications
- Explores classical models in ecology and evolution
- Questions at the end of every chapter
- Primers cover important mathematical topics
- Exercises with answers
- Appendixes summarize useful rules
- Labs and advanced material available
Book Description
Occupancy Estimation and Modeling is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models.
* Provides authoritative insights into the latest in estimation modeling
* Discusses multiple models which lay the groundwork for future study designs
* Addresses critical issues of imperfect detectibility and its effects on estimation
* Explores the role of probability in estimating in detail
Customer Reviews:
Great book for understanding site occupancy modeling.......2006-03-10
I think this is a pretty good book. It is the only reference on this relatively new type of patch occupancy modeling. It is mainly focused on the models of MacKenzie et al. and the Royle and Nichols model. This is a great place to start if you know nothing about this method or a good reference for advanced users.
This book does not fill the need of an introductory "how-to" book. If you want to know how to set up models and run them in program PRESENCE or MARK you will need to wait. Such a book does not exist. This is not a cookbook, but a compilation of the theory and an explanantion of the methodology behind occupancy estimation.
Book Description
From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology.
Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians.
Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Customer Reviews:
An excellent recent overview of modeling.......2007-06-14
This is an excellent book for students or faculty interested in learning more about the current state of the art in modeling of biological systems. The authors make a great effort to keep the mathematical sophistication at a level that students (or faculty) who primarily have a biological background will still be able to follow in some detail. They are also able to suggest some of the exciting current areas of research and new areas for the future. All in all, well worth reading if you are interested in the topic of modeling of biological systems.
Book Description
Individual-based models are an exciting and widely used new tool for ecology. These computational models allow scientists to explore the mechanisms through which population and ecosystem ecology arises from how individuals interact with each other and their environment. This book provides the first in-depth treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call "individual-based ecology."
Grimm and Railsback start with a general primer on modeling: how to design models that are as simple as possible while still allowing specific problems to be solved, and how to move efficiently through a cycle of pattern-oriented model design, implementation, and analysis. Next, they address the problems of theory and conceptual framework for individual-based ecology: What is "theory"? That is, how do we develop reusable models of how system dynamics arise from characteristics of individuals? What conceptual framework do we use when the classical differential equation framework no longer applies? An extensive review illustrates the ecological problems that have been addressed with individual-based models. The authors then identify how the mechanics of building and using individual-based models differ from those of traditional science, and provide guidance on formulating, programming, and analyzing models. This book will be helpful to ecologists interested in modeling, and to other scientists interested in agent-based modeling.
Customer Reviews:
Thorough.......2007-09-10
I haven't finished the book, but so far it's been very thorough on the subject and has given me lots of ideas for how to proceed on the project I'm working on. Would definitely recommend it.
Book Description
A comprehensive guide to investment guarantees in equity-linked life insurance
Due to the convergence of financial and insurance markets, new forms of investment guarantees are emerging which require financial service professionals to become savvier in modeling and risk management. With chapters that discuss stock return models, dynamic hedging, risk measures, Markov Chain Monte Carlo estimation, and much more, this one-stop reference contains the valuable insights and proven techniques that will allow readers to better understand the theory and practice of investment guarantees and equity-linked insurance policies.
Mary Hardy, PhD (Waterloo, Ontario, Canada), is an Associate Professor and Associate Chair of Actuarial Science at the University of Waterloo and is a Fellow of the Institute of Actuaries and an Associate of the Society of Actuaries, where she is a frequent speaker. Her research covers topics in life insurance solvency and risk management, with particular emphasis on equity-linked insurance. Hardy is an Associate Editor of the North American Actuarial Journal and the ASTIN Bulletin and is a Deputy Editor of the British Actuarial Journal.
Customer Reviews:
Very Helpful.......2004-07-17
As guarantee products are popping up all over the global banking and insurance markets, it is absolutely essential to ensure that the proper financial values are upheld in order to avoid many of the problems that the North American market has faced. 'Investment Guarantees' does a wonderful job of describing these risks in simple enough terms that the pages can be quoted to both financial and non financial people. A very powerful read for those looking to undersand the value of guarantees that are placed on accumulation type insurance products.
Book Description
Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.
Customer Reviews:
Practical text on extreme values statistics.......2006-11-10
Stuart Coles, who is well published on the open literature, has delivered this practical text on extreme values statistics by providing extreme values theory in a simplified manner with worked examples. Further, Coles has removed much of the complicating aspects (long mathematical proofs and overly complex notation) typical of statistical literature. The text provides any engineer or scientists with the tools required to complete routine extreme values analysis.
A clearly written intro book on extremes.......2002-10-05
I recently used the software accompanied to this book kindly made available by the author and was led to know more about this book and the author's other works. I like what I saw and think the author has done a supeb job in explaining the difficult theory in plain language and in the context of data analysis. Thus it is an "action" book instead of the "just theory" as with most other books. The book provides a balanced treatment of different approaches to extreme value analysis. Personally I prefer the generalized Pareto approach, though theoretically the point process approach may be very neat, if it can be realized.
I think extreme value theory in general is an important statistical area, since in practice one may be forced to deal with analyzing extreme events, such as in financial engineering, environmental or climate analysis, or network design. I wholeheartedly recommend this book for anyone who want to learn this area from one of the leading researchers.
well written with a nice mix of theory and application.......2002-01-29
This book is the most current text available on the theory of extreme values. The author eloquently provides us with an understanding of the theory and it vast applications. It is intended for researchers students and practitioners. So it provides an in-depth account of the theory with many real world examples. It contains an excellent up-to-date bibliography. Important theorems are presented with their implications but without mathematical proofs. Computations are done in SPlus. The author provides an appendix on computational aspects that tells the reader where to go to download examples and find the SPlus functions that are used.
Topics include classical extreme value theory and models, threshold models, extremes in dependent stationary cases, extremes for some nonstationary stochastic processes, the point process approach, multivariate extremes and some special topics including extremes in spatial processes and the Bayesian approach to extremes (with examples employing MCMC methods).
Average customer rating:
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Dynamic Modeling in Behavioral Ecology
Marc Mangel , and
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Dynamic State Variable Models in Ecology: Methods and Applications (Oxford Series in Ecology and Evolution)
ASIN: 0691085064 |
Book Description
This book describes a powerful and flexible technique for modeling of behavior, based on evolutionary principles.
Average customer rating:
- Well, it isn't a pop science book
- An interesting, but very thick, book
- Infomercial
- Gould has formulated the real new kind of science
- There is no "New Kind of Science" here
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A New Kind of Science
Stephen Wolfram
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Sync: The Emerging Science of Spontaneous Order
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Emergence: From Chaos to Order (Helix Books)
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Release Date: 2002-05-14 |
Amazon.com
Physics and computer science genius Stephen Wolfram, whose Mathematica computer language launched a multimillion-dollar company, now sets his sights on a more daunting goal: understanding the universe. Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems.
On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day.
Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton
Book Description
This long-awaited work from one of the world's most respected scientists presents a series of dramatic discoveries never before made public. Starting from a collection of simple computer experiments---illustrated in the book by striking computer graphics---Wolfram shows how their unexpected results force a whole new way of looking at the operation of our universe.
Wolfram uses his approach to tackle a remarkable array of fundamental problems in science: from the origin of the Second Law of thermodynamics, to the development of complexity in biology, the computational limitations of mathematics, the possibility of a truly fundamental theory of physics, and the interplay between free will and determinism.
Written with exceptional clarity, and illustrated by more than a thousand original pictures, this seminal book allows scientists and non-scientists alike to participate in what promises to be a major intellectual revolution.
Customer Reviews:
Well, it isn't a pop science book.......2007-06-25
I just finished the book, and yes it is big, but actually easy to read. This book is not written or edited to achieve popularity among casual readers. But this does not seem to be Wolfram's goal. I must admit he seems arrogant and overconfident in the first pages, but that mellows, and you realize he is writing with a goal of clarity and consciously avoids modesty for this reason (he addresses this in his notes).
In short, truly a novel concept and worthy of the title even if some of his predictions don't materialize. Clearly in the scienctific world (especially in complexity, which is the next major hurdle in science) we need some new approaches, and Wolfram does an amazing job of delving into multiple areas to pursue this. This man has worked hard with a goal of a new idea, and he has the history of success to back it up. It is amazing that a person who possibly could be one of the most proficient in advanced practical mathematics seems to be telling us we need to find a new road to solving complexity problems in different ways than using a conventional mathematical approach.
History will tell if this man has somthing profound or is just off on a tangent, but my feeling is that he has many ideas that are true. A good book if you are an innovative thinker, but surely it will be boring to a conventional mind.
An interesting, but very thick, book.......2006-12-15
My very first reaction to this book was the reverse of my reaction to the back cover of "1066 and all that." That book cited a "review" by "the bookworm" which read "...this thin volume..." Obviously, I had to laugh. And when I saw this book, needless to say, my first thought was of that bookworm saying "...yum, this is the kind of book I can really dig into!"
Yes, this volume is too thick. But my review has to be about the contents.
I would like to admit that I know something about the theory of computation, that I have taught university classes on the subject, and that I have discovered a few things of my own about it. Maybe that is why I found the book reasonably easy to read. And in fact, one benefit of having all this material is that one can see what Wolfram did and follow his reasoning.
It is true that pretty much all computer science students, certainly including those who took one of my classes, know that simple programs can produce very highly ordered output, very highly complex output, or very irregular output. But that does not mean that Wolfram has accomplished nothing by discovering some more examples of this. I found this work to be intriguing.
Still, there are some problems with the book, as many of the other reviewers have pointed out. And quite a few are valid. On the other hand, I am not quite so willing to accept the complaints about Wolfram merely trying all sorts of ideas on a computer and seeing what happens. I think that computer searches are excellent ways to discover things. And I think Wolfram's tale is worth something by showing this.
Wolfram says he believes that "no system can ever carry out explicit computations that are more sophisticated than those carried out by systems like cellular automata and Turing machines." Um, that sounds fair. After all, any computable function can be produced by a Turing machine. It does not seem unreasonable to say that any process in the universe can be represented by computable functions.
I like the chapter on fundamental physics. Here, Wolfram suggests using automata as models to explain the very existence of fundamental physical laws. That still leaves unanswered such questions as why matter exists. But I think it is a good way to look at things.
Wolfram makes the point that some of his rules do not obey the Second Law of Thermodynamics. That is not a surprise to me. After all, chaos does tend to produce order! I think that stable, self-repairing, regenerating, and reproducing entities ought to quickly dominate any scene; everything else decomposes, decays, or gets eaten. And that should not look like it complies with the Second Law of Thermodynamics!
There is also a chapter on "implications for everyday systems." The most obvious is natural selection in biology. Here, I feel from the work I have done that iterative improvement is extremely, even surprisingly, powerful. That iterative improvement can be made to sequences. But while the initial sequence might be generated by a simple cellular automaton, the mutations would be in the product of the cellular automaton, not (as I think Wolfram implies) in the automaton itself. Wolfram argues that biological complexity can be generated by rather simple means. I think this is a good point. Even though DNA may look pretty complex to us, it is much easier if the biological growth pattern follows an intrinsically simple algorithm. I would add that natural selection can add to some of the apparent complexity.
Nevertheless, this book is simply too big. It needed better editing. There needed to be better references to previous work. Basic facts about automata needed to be better separated from the variety of speculations connecting them to so many other fields.
I guess this book is stimulating. So I can't simply give it one star. But just as Newton's Principia suffered from so much polishing as to make it almost unreadable, this book suffers from way too little polishing. I'll settle for three stars.
Infomercial.......2006-11-10
I am amazed that serious scientists spent many hours writing reviews of this book, here and elsewhere on the Internet.
Don't they see that this is an infomercial, designed solely to sell more copies of Mathematica?
Gould has formulated the real new kind of science.......2006-10-04
Anyone interested in finding out about the real nature of biological diversity
should read " The Structure of Evolutionary Theory" by Stephen Jay Gould.
There is no "New Kind of Science" here.......2006-07-30
I was very disappointed by this book. I was very eager to read this book and learn about Wolfram's thoughts about a new approach to science, but there is nothing in this book that justifies such a lofty title. The first six chapters of the book describe various types of cellular automata, which are computational models whose evolutionary behavior is governed by simple rules. Wolfram devoted much of his scientific career studying such systems.
One particular system that has caught Wolfram's interest is a cellular automata model that he calls "Rule 30". What is so remarkable about "Rule 30"? Rule 30 starts off with a single element and, through repeated application of a simple rule, rapidly evolves into a system involving apparently complex behavior.
There were two big problems with the first six chapters of this book. First, Wolfram never bothers to try to explain what is so special about "Rule 30". Why doesn't Rule 29 or Rule 31 show complex behavior? What are the essential elements of Rule 30 that give complexity? Wolfram gives no information on this. A second big problem is that Wolfram is much too repetitive. The point that complexity can be generated by simple rules is hammered in over and over again. This book could clearly have used a lot of editing.
The rest of the book describes ways in which cellular automota can by applied to various scientific problems, but no concrete, specific pathways are given for how to tackle these various problems. Rather, the chapters consist of various suggestions and musings by Wolfram on how cellular automata may relate to a wide range of scientific problems. The breadth of his discussions is more impressive than their depth, which often never gets much deeper than noting visual similarities between some of his diagrams and the physical appearances of shells, tiger stripes, snowflakes, etc.
A key problem with this book is that it never attempts to analyze why some rules give "complex" behavior and why others don't. I'm reminded of a story that Wolfram himself related about the time he showed Richard Feynman a printout of "Rule 30":
"Well, we'd just been crawling around the floor--with help with some other people--trying to use meter rules to measure some feature of a giant printout of it. And Feynman takes me aside, rather conspiratorially, and says: "Look, I just want to ask you one thing: how did you know rule 30 would do all this crazy stuff?" "You know me," I said. "I didn't. I just had a computer try all the possible rules. And I found it." "Ah," he said, "now I feel much better. I was worried you had some way to figure it out." "
Both Wolfram and Feynman are geniuses, but Feynman had something that Wolfram doesn't: A gift for seeing past the unessentials and getting to the gist of a problem. Wolfram fills his books with countless pages of diagrams showing "complex" behavior, but never tries to explain the essential nature of Rule 30.
After reading this book, I'm also reminded of a statement by Freeman Dyson, another brilliant scientist, after he looked at Wolfram's book: "There's a tradition of scientists approaching senility tocome up with grand, improbable theories. Wolfram is unusual in that he's doing this in his 40's".
Book Description
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Customer Reviews:
Good book for computational neuroscience.......2007-01-28
I am a mathematician and economist interested in how human brain works. To me, (so far) this is the best book using equations to describe the overall picture of brain functions. Even though it might not touch in-depth research topics, I am sure it gives anyone interested in neuroscience very solid foundations on which more advance topics are built. (It actually invites me to more in-depth research topics, such as reinforcement learning, reward-punishment system, etc.)
If math is your familiar language (says, system of differential equations and Bayesian probability), and you are interested to know, in technical details, how the brain functions, this book is for you. Then, I think, you can go into research topics of your interests after finishing reading this book.
"Theoretical Neuroscience" Dry but Informative.......2006-03-23
"Theoretical Neuroscience" is an in-depth introduction to modeling of neural systems from the chemical/electrical processes within neurons, up through small networks of neurons. It is a little dry, but provides a wealth of information on modeling the electrophysical and computational properties of neurons.
Good starting point for undergraduate students.......2005-07-05
This book covers a wide range of different and important subjects of this field and provides by this a good overview to students new in neuroscience. On the other hand side, the topics discussed are not described thoroughly, but stay on the surface. This maybe no big problem for undergraduates who try just to understand the basics but certainly this is not satisfactory for more advanced students or researches.
In my opinion, this book blurs the view of the reader by presenting results about experiments and theoretical models side by side in a way that no fair and solid discussion is provided indicating clearly the limitations and problems of current models. By this, one could get the feeling that the presented models are more than tool to analyse data. However, exactly this is not true for most of the models as can be seen by the fact that these models can also be found in other areas than neuroscience with other interpretations.
Theoretical Neurosciences from a Computational Perspective.......2004-06-11
This text will become a standard course book for Graduate Schools in Computational Neurosciences. You need to know advanced engineering mathematics & probability theory to be able to understand this book. Dayan & Abbott model primary visual cortical, MT, LIP, and Motor cortical neurons as single units, but also as populations (clusters) of firing cells. They discuss Bayes Theorem, probability theory as it applies to the brain, and parietal lobe function as well. They derive all the equations associated with these models for the student so that more advanced parts of the book are comprehensible. The book is not meant to be a general Neuroscience book, but rather a course book about neuronal modeling, computational neurobiology, and neural engineering. It serves these three purposes well. In my opinion, this is the best written account of neuron modeling out there for the graduate student and researcher. Methods in Neuronal Modeling by Christof Koch is the other great book on this subject. If you own these two books you should be able to advance in high level neural modelling. There are numerous equations and formulae of interest throughout each chapter in these two volumes. The price of 39.00 USD for the hardcover is really quite a bargain.
Great textbook and reference.......2003-08-16
This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.
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 :-) .
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- Anatomy of Movement
- Animal Behavior: An Evolutionary Approach, Eighth Edition
Books Index
Books Home
Recommended Books
- The Voice That Challenged a Nation: Marian Anderson and the Struggle for Equal Rights
- The Illuminatus! Trilogy: The Eye in the Pyramid, The Golden Apple, Leviathan
- Sharpe's Eagle
- Parsley, Sage, Rosemary and Crime: A Pennsylvania Dutch Mystery with Recipes
- Photography Speaks: 150 Photographers On Their Art
- Molecular Modeling and Simulation
- RFID+ Exam Cram
- Art and Photography: Forerunners and Influences
- Living with Michigan's wetlands: A landowner's guide
- Mysterious Beauty: Desert Plants and Cacti of the Americas
Evolutionary Dynamics: Exploring the Equations of Life
The Origins of Genome Architecture
Individual-based Modeling and Ecology (Princeton Series in Theoretical and Computational Biology)
The Theoretical Biologist's Toolbox: Quantitative Methods for Ecology and Evolutionary Biology
Evolutionary Theory
