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This volume is based on the proceedings of the International Workshop on Dynamical Systems and their Applications in Biology held at the Canadian Coast Guard College on Cape Breton Island (Nova Scotia, Canada). It presents a broad picture of the current research surrounding applications of dynamical systems in biology, particularly in population biology.

The book contains 19 papers and includes articles on the qualitative and/or numerical analysis of models involving ordinary, partial, functional, and stochastic differential equations. Applications include epidemiology, population dynamics, and physiology.

The material is suitable for graduate students and research mathematicians interested in ordinary differential equations and their applications in biology. Also available by Ruan, Wolkowicz, and Wu is Differential Equations with Applications to Biology, Volume 21 in the AMS series Fields Institute Communications. ... Read more

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The book shows how mathematical and computational models can be used to study cancer biology. It introduces the concept of mathematical modeling and then applies it to a variety of topics in cancer biology. These include aspects of cancer initiation and progression, such as the somatic evolution of cells, genetic instability, and angiogenesis. The book also discusses the use of mathematical models for the analysis of therapeutic approaches such as chemotherapy, immunotherapy, and the use of oncolytic viruses. ... Read more

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"The book is mathematically very clever although it uses onlyoccasional techniques beyond the basic probability and statistics... it clearly demonstrates that new biomedical knowledge does emergefrom the stochastic modeling of cancer development ... thisinteresting book is a noticeable event in biomathematicsand biostatistics in general, and in carcinogenesis modeling inparticular." ... Read more

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The chemostat is a basic piece of laboratory apparatus, yet it has begun to occupy an increasingly central role in ecological studies.The ecological environment created by a chemostat is one of the few completely controlled experimental systems for testing microbial growth and competition. As a tool in biotechnology, the chemostat plays an important role in bioprocessing. This book presents the theory of the chemostat as a model for larger ecological problems suchas food chains, competition along a gradient, competition in the presence of an inhibitor, and the effects of time varying inputs.Mathematical models that take account of size structure, variable yields, and diffusion are also considered.The basic phenomenaare modeled and analyzed using the dynamical systems approach.New directions for research and open problems are discussed. Six appendices provide an elementary description of the necessary mathematical tools. ... Read more

Customer Reviews (2)

The Theory of Chemostat:Dynamics of Microbial Competition
"The best mathematical book I've ever read, Hal is truly the best".

The Theory of Chemostat:Dynamics of Microbial Competition
"The best mathmatical book I've ever read, Hal is truly the best". ... Read more

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During the past decade, geneticists have cloned scores of Mendelian disease genes and constructed a rough draft of the entire human genome. The unprecedented insights into human disease and evolution offered by mapping, cloning, and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists.

Mathematical and Statistical Methods for Genetic Analysis is written to equip students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to population genetics, gene mapping, risk prediction, testing of epidemiological hypotheses, molecular evolution, and DNA sequence analysis. Many specialized topics are covered that are currently accessible only in journal articles.

This second edition expands the original edition by over 100 pages and includes new material on DNA sequence analysis, diffusion processes, binding domain identification, Bayesian estimation of haplotype frequencies, case-control association studies, the gamete competition model, QTL mapping and factor analysis, the Lander-Green-Kruglyak algorithm of pedigree analysis, and codon and rate variation models in molecular phylogeny. Sprinkled throughout the chapters are many new problems.

Kenneth Lange is Professor of Biomathematics and Human Genetics at the UCLA School of Medicine. At various times during his career, he has held appointments at the University of New Hampshire, MIT, Harvard, and the University of Michigan. While at the University of Michigan, he was the Pharmacia & Upjohn Foundation Professor of Biostatistics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, and applied stochastic processes. Springer-Verlag published his book Numerical Analysis for Statisticians in 1999. ... Read more

Customer Reviews (5)

great book where few texts of a statistical nature exist
This second edition updates the first with the many advances in the rapidly growing field of genetics. It provides a nice treatment of the mathematical and stochastic models that are useful in genetic studies.
It is a little disappointing that it does not go into the microarray technology that has become so important for experimentation in the last few years. Other recent books that cover statistical aspects of genetic research are Weir (1996) "Genetic Data Analysis II" Sinauer Associates (publisher) and Yang (2000) "Introduction to Statistical Methods in Modern Genetics" Gordon and Breach Science Publishers.

Read this book and you might learn something
This book is not for the novice dabbling in statistical genetics.This is a highly sophisticated, thought provoking book targeted to individuals with considerable mathematical ability and training. As such, this book is an invaluable tool for individuals hoping to make a real impact in the field of statistical genetics.I particularly enjoyed the chapter on Markov Chain Monte Carlo methods for pedigree data.

OK, but not for me
I'm interested in molecular genetics, this seems to be more about population genetics. There is some material, towards the back, about phylogeny. I can bash that a bit to make it match my needs, but it's still a bit of a stretch.

It seems to be a pretty good presentation of population genetics, the kind of genetics taught in high schools in the 70s. I can't comment on this book's merits, but I can warn the biochem types to spend their money elsewhere.

Mathematical Details of Genetics
This book has an excellent coverage on the mathematical subtlies of genetics. The complicated theories are complimented by numerous examples. The exercise at the end of each chapter has a collection of probing questions that tests the understanding of the topics covered in the respective chapter. However, there is no discussion on association studies and quantitative traits which are two of the most active areas of genetic epidemiology.Moreover, the coverage on linkage is not adequate.

Mathematical details about genetics
The book has an excellent coverage of the mathematical subtlities of genetics. Theories are illustrated through numerous examples and the exercises at the end of each chapter containprobing questions which would test the understanding of the topics covered in the respective chapters. However, there are no chapters devoted to quantitative traits and association analysis, which are currently active areas of research. The coverage of linkage is also not adequate. ... Read more

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Using theory, applications, and examples of inferences, Niche Modeling: Predictions from Statistical Distributions demonstrates how to conduct and evaluate niche modeling projects in any area of application. It features a series of theoretical and practical exercises for developing and evaluating niche models using the R statistics language. The author discusses applications of predictive modeling methods with reference to valid inferences from assumptions. He elucidates varied and simplified examples with rigor and completeness. Topics include geographic information systems, multivariate modeling, artificial intelligence methods, data handling, and information infrastructure.

Above all, successful niche modeling requires a deep understanding of the process of creating and using probability. Off-the-shelf statistical packages are tailored exactly to applications but can hide problematic complexities. Recipe book implementations fail to educate users in the details, assumptions, and pitfalls of analysis, but may be able to adapt to the specific needs of each study. Examining the sources of errors such as autocorrelation, bias, long term persistence, nonlinearity, circularity, and fraud, this seminal reference provides an understanding of the limitations and potential pitfalls of prediction, emphasizing the importance of avoiding errors. ... Read more

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Utilizing high speed computational methods to extrapolate to the rest of the protein universe, the knowledge accumulated on a subset of examples, protein bioinformatics seeks to accomplish what was impossible before its invention, namely the assignment of functions or functional hypotheses for all known proteins.

The Ten most Wanted Solutions in Protein Bioinformatics considers the ten most significant problems occupying those looking to identify the biological properties and functional roles of proteins.

- Problem One considers the challenge involved with detecting the existence of an evolutionary relationship between proteins.- Two and Three studies the detection of local similarities between protein sequences and analysis in order to determine functional assignment. - Four, Five, and Six look at how the knowledge of the three-dimensional structures of proteins can be experimentally determined or inferred, and then exploited to understand the role of a protein. - Seven and Eight explore how proteins interact with each other and with ligands, both physically and logically.- Nine moves us out of the realm of observation to discuss the possibility of designing completely new proteins tailored to specific tasks. - And lastly, Problem Ten considers ways to modify the functional properties of proteins.

After summarizing each problem, the author looks at and evaluates the current approaches being utilized, before going on to consider some potential approaches.

introbul>Features---------------------Features---------------------· Presents introductory material on protein structure and function, with an evolutionary perspective· Describes ten of the most cogent problems in computational biology· Considers future routes that are likely to improve our understanding of the exquisitely specific and efficient mechanisms of protein function· Includes a suggested reading list for further research at the end of each chapter· ... Read more

Customer Reviews (3)

Useful, but the title doesn't really describe it
The title of this book is misleading; at least, it misled me. Before opening it I thought it would deal with ten unsolved problems in protein bioinformatics that we should like to be able to solve but at present cannot. In other words, I expected that it would be a book for researchers that would challenge them to find solutions to major problems where none are currently available. I was, however, surprised that there should be as many as ten of these. In fact, this is not really a book for researchers, but one for students and others new to protein bioinformatics: these are the things we will want to know when we approach a protein with a bioinformatic approach, these are the sort of methods currently in use, this is the sort of information we can get from them, and these are the respects in which we may hope they will be improved in the future. In short, we are not dealing with questions that at present have no answers, but with ones that we may hope to be able to answer better. In this respect, however, protein bioinformatics is just like any other discipline: few, if any of the methods we use in science are so good that we cannot conceive of anything better.

As an example, Problem 1 concerns protein sequence alignment: the account begins with a discussion of protein evolution, leading to the distinction between orthology and paralogy and the ideas of protein families, similarity matrices and gap penalties. The chapter then proceeds to a description, at times quite advanced, of methods in current use for comparing and aligning sequences, including multiple alignments. Only in the last of nearly thirty pages discussing this problem does the author turn her attention to the ways in which the methods in use might be improved, but she provides almost no detail.

The other chapters deal with secondary-structure prediction from sequence information, prediction of biological function, tertiary-structure prediction, and so on, ending with more engineering topics such as the design of artificial proteins and the modification of existing proteins to fulfil novel functions. In all of these the presentation is competent, and the book will be very useful to anyone wanting to learn about protein bioinformatics, in particular about the state of the art today. On the other hand, with none of the problems are we dealing with a "most wanted solution" in the sense of seeking a way ahead when the road appears at present to be completely blocked. Nowhere does the author throw down her gauntlet before her colleagues, saying this is where you have failed, and must provide a solution to this vital problem.

Depends on what you want
This book delivers reasonably well on the promise in its title: it does a good job in stating the most computational interesting problems relating to proteins. It assumes the reader knows a little about biochemistry, biology, and computational techniques, but only a little about each. Given that base, it does a fair job in describing problems related to protein structure, function, analysis, and design. It's not an advanced text, in either its computational or biological sides, but not an elementary introduction, either. Someone a bit above novice level will probably get the most out of it.

A few things left me a bit leery about this text, though. Despite its 2005 copyright date, the author (p.53) cites an estimate of human 50,000 genes. I'm not sure where (or when) that number comes from, because most estimates today are closer to 30,000. There was another a minor annoyance in the discussion of convolution as a tool in protein docking. The failure to distinguish convolution from correlation is minor and forgivable. Saying that one "convolutes" a convolution is like say that one "revolutes" a revolution. Revolve: revolution, convolve: convolution. Also, the Fourier transform step in correlation, especially when docking a small molecule to a protein, is an optimization rather than a requirement. Transform-based correlation gives better performance for asymptotically large models. In some computing environments, for models of realistic sizes, the simplicity of direct correlation gives a performance advantage - and allows non-linear scoring algorithms that would be impossible with the transform approach.

This is a fair introduction to many of the ways people study proteins computationally, and to the kinds of tools required. There is very little computaitonal detail, however. It may help a tool-builder create a conceptual base for studying proteins, but won't help much with the specific calculations.

//wiredweird

Comprehensive but a little dated
This is a very useful overview of the very broad subject of bioinformatics, and it provides a good background on a variety of approaches to topics like protein conformation prediction.The translation is excellent - the subject-matter is clear and there are no obvious errors, which is unusual for such a technical subject.The main drawback of the book is that, because this is a field in which progress is being made rapidly, the book is already out of date in places.For example, in the chapter dealing with protein structure prediction, there is scant mention of the most successful approach to date, namely the Rosetta project initiated at the University of Washington in Seattle.

Nevertheless, this is a very useful primer for people coming into the area of bioinformatics and it covers topics that will not age as rapidly, such as certain statistical models.Indeed, the author's exposition of how Hidden Markov Models work is as clear as anything I've read anywhere. ... Read more

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Biomolecular Simulation presents rigorous descriptions of various biomolecular simulation techniques and sampling methods. Using these techniques, it provides a thorough understanding of the microscopic details underlying how biomolecules work. The book focuses on the concept of free energy and on real problems related to protein structure and function. Discussions include physico-chemical concepts and simulation techniques. The authors provide exercises and examples throughout the book as well as a CD-ROM to to help readers in the practical implementation of methods in a production simulation package (ADUN). ... Read more

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This textbook is concerned with the mathematical modeling of biological and physiological phenomena for mathematically sophisticated students. A range of topics are discussed: diffusion population dynamics, autonomous differential equations and the stability of ecosystems, biogeography, pharmokinetics, biofluid mechanics, cardiac mechanics, the spectral analysis of heart sounds using FFT techniques. The last chapter deals with a wide variety of commonly used medical devices. This edition includes new chapters on epidemiology, including modeling the spread of AIDS through a population. Coverage is based on courses taught by the author over many years and the material is class tested. The reader is aided by many exercises that examine key points and extend the presentation in the body of the text. All students of mathematical biology will find this book to be a highly useful resource. ... Read more

Customer Reviews (1)

Lacking in depth
This book lacks depth. The explanations of many important biological concepts do not seem to go beyond mere definitions of terms. Some parts of the book appear to be cut-and-paste passages from the author's research papers in journals. If you are hoping to learn some real stuffs on mathematical biology, it is better to look elsewhere. In my opinion, this book is at best a mediocre attempt at imitating SI Rubinow's "Introduction to Mathematical Biology". ... Read more

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This is the first monograph to present a unified approach tousing mathematical models in the study of qualitative and quantitativeregularities of immune response dynamics in infectious diseases withinindividual organisms. These mathematical models are formulated assystems of delay- differential equations.
Simple mathematical models of infectious diseases, antiviral immuneresponse and antibacterial response were developed and applied to thestudy of hepatitis B, influenza A, infectious bacterial pneumonia, andmixed infections.
Particular attention was paid to the development of efficientcomputational procedures for solving the initial value problem forstiff delay-differential equations and to the parameter identificationproblem. Adjoint equations and the perturbation theory were used forthe sensitivity analysis.
Audience: This book will be of interest to a wide range ofmathematicians and specialists in immunology and infectious diseases.It can also be recommended as a textbook for postgraduate students,bridging the gap between mathematics, immunology and infectiousdiseases research. ... Read more

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This introduction to mathematical methods that are useful for studying population phenomena is intended for advanced undergraduate and graduate students, and will be accessible to scientists who do not have a strong mathematics background. The material is graded in mathematical difficulty. The earlier parts of the book involve elementary diference equations while later chapters present topics that require more mathematical preparation. Models of total population and population age structure are first derived and studied, and then models of random population events are presented in terms of Markov chains. The last two chapters deal with mathematical methods used to uncover qualitative behaviour of more complicated difference equations. Each chapter begins with a simple model, usually of some historical interest, that defines the primary goals of the chapter. Exercises, for which solutions are provided, illustrate material in the text and also deal with models more advanced than those derived and studied in the text. ... Read more

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Each summer the IAS/Park City Mathematics Institute Graduate Summer School gathers some of the best researchers and educators in a particular field to present lectures on a major area of mathematics. A unifying theme of the mathematical biology courses presented here is that the study of biology involves dynamical systems. Introductory chapters by Jim Keener and Mark Lewis describe the biological dynamics of reactions and of spatial processes. Each remaining chapter stands alone, as a snapshot of in-depth research within a sub-area of mathematical biology. Jim Cushing writes about the role of nonlinear dynamical systems in understanding complex dynamics of insect populations. Epidemiology, and the interplay of data and differential equations, is the subject of David Earn's chapter on dynamic diseases. Topological methods for understanding dynamical systems are the focus of the chapter by Leon Glass on perturbed biological oscillators. Helen Byrne introduces the reader to cancer modeling and shows how mathematics can describe and predict complex movement patterns of tumors and cells. In the final chapter, Paul Bressloff couples nonlinear dynamics to nonlocal oscillations, to provide insight to the form and function of the brain. The book provides a state-of-the-art picture of some current research in mathematical biology. Our hope is that the excitement and richness of the topics covered here will encourage readers to explore further in mathematical biology, pursuing these topics and others on their own. The level is appropriate for graduate students and research scientists. Each chapter is based on a series of lectures given by a leading researcher and develops methods and theory of mathematical biology from first principles. Exercises are included for those who wish to delve further into the material. ... Read more

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Volume 100 which is the final volume of the LNBM series serves to commemorate the achievements in two decades of this influential collection of books in mathematical biology. The contributions, by the leading mathematical biologists, survey the state of the art in the subject, and offer speculative, philosophical and critical analyses of the key issues confronting the field. The papers address fundamental issues in cell and molecular biology, organismal biology, evolutionary biology, population ecology, community and ecosystem ecology, and applied biology, plus the explicit and implicit mathematical challenges. Cross-cutting issues involve the problem of variation among units in nonlinear systems, and the related problems of the interactions among phenomena across scales of space, time and organizational complexity. ... Read more

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Abstract and conceptual models have become an indispensable tool for analyzing the flood of highly detailed empirical data generated in recent years by advanced techniques in the biosciences. Scientists are developing new modeling strategies for analyzing data, integrating results into the conceptual framework of theoretical biology, and formulating new hypotheses. In Modeling Biology, leading scholars investigate new modeling strategies in the domains of morphology, development, behavior, and evolution.

The emphasis on models in the biological sciences has been accompanied by a new focus on conceptual issues and a more complex understanding of epistemological concepts. Contributors to Modeling Biology discuss models and modeling strategies from the perspectives of philosophy, history, and applied mathematics. Individual chapters discuss specific approaches to modeling in such domains as biological form, development, and behavior. Finally, the book addresses the modeling of these properties in the context of evolution, with a particular emphasis on the emerging field of evolutionary developmental biology (or evo-devo).

Contributors:
Giorgio A. Ascoli, Chandrajit Bajaj, James P. Collins, Luciano da Fontoura Costa, Kerstin Dautenhahn, Nigel R. Franks, Scott Gilbert, Marta Ibañes Miguez, Juan Carlos Izpisúa-Belmonte, Alexander S. Klyubin, Thomas J. Koehnle, Manfred D. Laubichler, Sabina Leonelli, James A. R. Marshall, George R. McGhee Jr., Gerd B. Müller, Chrystopher L. Nehaniv, Karl J. Niklas, Lars Olsson, Eirikur Palsson, Daniel Polani, Diego Rasskin Gutman, Hans-Jörg Rheinberger, Alexei V. Samsonovich, Jeffrey C. Schank, Harry B. M. Uylings, Jaap van Pelt, and Iain Werry ... Read more

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Spatio-temporal pattern formation is a major area of research within the subject of mathematical biology. The topic involves the use of mathematical modelling to analyse how patterns in biology are created and develop. For example, the growth, over time, of the intricate and beautiful patterns on certain sea-shells or the striped markings on a tiger can be modelled and their development predicted in terms of nonlinear mathematical processes. The current volume captures the breadth of recent research into various aspects of spatio-temporal pattern and form, such as development biology, reaction-diffusion systems and morphometrics.

Brings the ideas of the classic On Growth and Form by D'Arcy Thompson, the founding classic of mathematical biology, fully up to date and looks to future developments in the subject.
* Foreword provided by Professor John Tyler Bonner, Princeton University.
* World class collection of internationally renowned contributors from both experimental and theoretical backgrounds

Taking its inspiration from D'Arcy Thompson's classic and still influential volume On Growth and Form, this new volume presents a collection of 21 articles from the Plenary Speakers of the recent D'Arcy Thompson Conference, held at the University of Dundee, 20-24 September 1998. The topics covered include pattern formation in development biology, reaction-diffusion systems, intercellular systems and morphometrics, offering the reader a stimulating blend of theory and experiment.

This book will be of particular interest to bio-mathematicians and development biologists. Paediatric clinicians, evolutionary biologists, orthodontists, anatomists, physiologists and many other members of the biology community will also benefit greatly from it. ... Read more

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With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery.

Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization.

This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.

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This general introduction to the mathematical techniques needed to understand epidemiology begins with an historical outline of some disease statisticsdating from Daniel Bernoulli's smallpox data of 1760. The authors then go on to describe simple deterministic and stochastic models in continuous and discrete time for epidemics taking place in either homogeneous or stratified (nonhomogeneous) populations. They offer a range of methods for constructing and analyzing models, mostly in the context of viral and bacterial diseases of human populations. These models are contrasted with models for rumors and macro-parasitic diseases. Questions of fitting data to models, and the use of models to understand methods for controlling the spread of infection, are discussed. Exercises and complementary results at the end of each chapter extend the scope of the text. ... Read more

Customer Reviews (4)

A Foundation Book
This book tells what we knew about the mathematics of epidemics up until 1990. The differential equations (beginning with Bernoulli and moving forward) are presented well. That is, the variables are defined in the text and not too many steps are skipped in the derivations. The high point in this type of epidemiology came in 1927, when Kermack and McKendrick wrote the continuous-time epidemic equations. Diseases were characterized by the parameter rho, the relative removal rate. Up until the 1990s, we were just fitting our data to this model, and estimating rho.

Along came 'computational biology', or 'agent-based models' or 'numerical methods'. After 1990, these new techniques allowed us to escape from the perfect-mixing assumption that caused the Kermack and McKendrick model to depart from reality. With computation, we were able to see the impact of social networks, targeted innoculuations, and to test the value of different intervention strategies. See March 2005 Scientific American. None of those advances are discussed in this book. As a historical treatise, however, it is a superb addition to the library.

Great Service
My book arrived before estimated time and in better condition than described.I am a math dork and given the choice I would purchase from this seller.

Excellent Text
This is a broad, wonderful introduction to the mathematics of epidemic modeling. The authors have done an outstanding job at pointing out the mathematics of both deterministic and stochastic epidemic models.

Careful study of this small text will prepare one for a serious look at the current research on the subject. This material is far from ``old fashioned" as one reviewer wrote, indeed, this text is a welcome introduction to the subject!

Old-fashioned
The authors mainly give an introduction how to do the calculations by hand for several epidemic models. However, some of their tricks for doing the calculations are not very interesting anymore in times where computers are available. More imporatant, they hardly ever explain why they choose a particular model, what they want to calculate and how to interpret the results of the calculations. So reading the book does not give you much insight in epidemic modelling, only in doing some calculation. Furthermore they ignore all kind of recent approaches in epidemic modelling. (Most references are quite old (before 1990) and the few more recent references are most of the time only mentioned without going into detail.) ... Read more

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This text presents mathematical biology as a field with a unity of its own, rather than only the intrusion of one science into another. It updates an earlier successful edition and greatly expands the concept of the "computer biology laboratory," giving students a general perspective of the field before proceeding to more specialized topics. The book focuses on problems of contemporary interest, such as cancer, genetics, and the rapidly growing field of genomics. It includes new chapters on parasites, cancer, and phylogenetics, along with an introduction to online resources for DNA, protein lookups, and popular pattern matching tools such as BLAST. In addition, the emerging field of algebraic statistics is introduced and its power illustrated in the context of phylogenetics.

A unique feature of the book is the integration of a computer algebra system into the flow of ideas in a supporting but unobtrusive role. Syntax for both the Maple and Matlab systems is provided in a tandem format. The use of a computer algebra system gives the students the opportunity to examine "what if" scenarios, allowing them to investigate biological systems in a way never before possible. For students without access to Maple or Matlab, each topic presented is complete. Graphic visualizations are provided for all mathematical results.

Mathematical Biology includes extensive exercises, problems and examples. A year of calculus with linear algebra is required to understand the material presented.  The biology presented proceeds from the study of populations down to the molecular level; no previous coursework in biology is necessary.  The book is appropriate for undergraduate and graduate students studying mathematics or biology and for scientists and researchers who wish to study the applications of mathematics and computers in the natural sciences.