| |||||||||
| Bioscience book menu |
|
 | ||
| By: Uri Alon ISBN: 1584886420 Publisher: Chapman & Hall/CRC Release Date: 07 July, 2006 Bioscience book rank: 45113 | I am a macromolecular crystallographer interested in theoretical systems biology, and this book is a real goldmine. It explains all the concepts behind biochemical systems and networks in a clear, lucid language. This book is a pleasure to read, for both biologists and mathematicians alike. I'm a Ph.D. student in biophysics. This is the best treatment of systems biology that I've encountered. It treats both the math and the biology with clarity, rigor, and respect. It simplifies without dumbing down. It's beautifully written. If you doubt that systems biology is a real scientific discipline, this book will change your mind. The history of science over the past few centuries is to become ever more specialized. The physicists, becomming ever more concerned with the very large (stars, galaxies, the cosmos) or the very tiny (first atoms, then atomic components, now sub-components. The biologists on the other hand were studying much larger things, such as the cells that make up life. Both sciences developed techniques to facilitate their study. <br /> <br />In recent years, researchers have discovered that sometimes these specialized techniques can be used to develop greater insight into what is happening in other sciences. <br /> <br />In this book, Dr. Alon uses his training in physics to examine certain aspects of biology and to use the terminology and mathematics to describe the way these biological networks work. <br /> <br />The goal of the book is to begin the formulation of general laws that apply to biological networks. This is done by providing a mathematical framework in which some of the design principles of biological systems can help to understand biological networks. In looking at the results, an underlying simplicity not seen before appears in biological systems. | |
 | ||
| By: Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit ISBN: 0387251464 Publisher: Springer Release Date: 31 August, 2005 Bioscience book rank: 305693 | I find this book is not so good for people without any gene or microarray experiment background. It didn't even give clear definition of the basic concepts.
<br />Another problem is that it's not well organized because every chapter is written by different authors who have different interest and preference and use slightly different terms for the same thing. If you're like me, you came upon this book because you decided to use R for analysis of microarray data, but you're mired in its gory and frustrating details. <br /> <br />Yes, you need a reference book. But not this one, and certainly not this edition. Better documentation can be found elsewhere (dare I say online?). <br /> <br />The code examples given are technically accurate and run as advertised, but they are of the "monkey see, monkey do" variety. They provide little intuition for how to use R for oneself, outside the covers of this text. For example, Chapter 23 discusses linear models for microarray data (using the "limma" package), and several code examples contain the parameter 'adjust = "fdr"'. The reader is never enlightened that this refers to a "false discovery rate" adjustment. <br /> <br />In other cases, example code is simply missing. Chapter 21 covers the Rgraphviz graphing library, with a figure showing the three common graphical layouts -- but no example code for producing these graphs is given (I had to find it outside the book). <br /> <br />For those trying to use R for computational biology, I recommend getting an overview of the R programming language first (Venables and Ripley's book "Modern Applied Statistics with S" is a great text), and only then wading into references such as this one, if at all. I purchased this book to learn specific details and look at applications for the functions present in bioconductor. I have had trouble applying some of the chapters to custom data because they are written for specific microarray/data formats. Overall, this book is a good value because it contains examples of how bioconductor can be used to aid in hypothesis testing, but I struggle to apply what I have read to the different types of data I have. The section on Statistical analysis for genomic experiments and the section on gaphs and networks should be the reason you purchase this book. They are very helpful and interesting. The case studies were not very helpful in my opinion. | |
 | ||
| By: Neil C. Jones, Pavel A. Pevzner ISBN: 0262101068 Publisher: The MIT Press Release Date: 01 August, 2004 Bioscience book rank: 79009 | Este livro é excelente por várias razões. Entre elas posso citar o fato de estar totalmente voltado ao aprendizado por exemplos, sempre de forma a relacionar um problema computacional com um problema em bioinformática. É um livro muito abrangente, cobre muito bem os tópicos relacionados a alinhamentos e comparações de sequências. Seu capítulo sobre Algoritmos com Grafos é o meu preferido. O autor consegue passar as noções fundamentais com muita simplicidade, de forma que qualquer pessoa possa aprender num ritmo bem rápido. This is the first book that I've read regarding bioinformatics, so Im updating this as my class moves along. You better have a grasp of basic data structures prior to beginning this book and background with a programming language as there is very little hand-holding in this text. A bio background makes it all more interesting but certainly is not critical. There are no sample code or sources printed with the book nor is there an included CD nor answers to exercises. There is an associated web site where some ideas may be had and errata found/reported, but its not very active that I have seen. The pseudo code in the book is very python-like so easy to make use of. I personally transfer the book's concepts to C/C++ (habit) without much problem, except sometimes my results differ from the book. Apparently these are book bugs, so be sure to check the web site out if unexpected things pop up. <br />Presently my class is in chapter 8 (of 12) and looking back I would like to caution that some data processing algorithms will drive a computer's CPU quite hard so be aware of battery-munching & heat. My only bones with this book so far are the alphabet soup of variables and lack of answers to exercises. It would be nice if variable definitions were refreshed at the beginning of pseudo code samples. <br />I like this book as an algorithms text over traditional texts because the applications are much more fascinating. Imagine searching for something and you don't know where that something is. On top of that add not even knowing exactly what it is you are looking for. And when you do find it, its not even in the data searched! This may sound unlikely or even impossible, but it is neither. Rather, its very cool. <br />4-stars I knew most of the stuff before I opened the first page. It's basically teaching data structures 101 using a few watered down bioinformatic problems for motivation. The lack of applied problems involving real data was most disappointing. It does have a lot of the type questions that some nerd (me one day :P) might ask you on a job interview. The questions are also a good way to kill time if you have nothing better to do. I give the book credit for stressing dynamic programming. I believe that this is one of the most important concepts in problem solving. <br /> <br />3 stars because I think it is a fairly good introduction for fledgling computer scientists BUT not a good reference for comptuer scientists trying to apply their skills to solve bioinformatic problems. <br /> <br /> | |
 | ||
| By: Dan Gusfield ISBN: 0521585198 Publisher: Cambridge University Press Release Date: 28 May, 1997 Bioscience book rank: 317913 | A well written text book with an obvious bias to biological application, but maybe most useful for its clear explanation and rigour of string algorithms. I bought this book not because I have any interest in computational biology but because at that time I had an interest in (and professional need for) extremely fast and efficient ways to search through massive data stores. In this I was not disappointed, having found thorough treatments of how to do exact pattern matching as well as various types of "closest" match searching though very large data sets in minimal time. <br /> <br />While perhaps overly theoretical for a person like myself who has not had extensive schooling, it certainly matched my expectations. <br /> <br />I would recommend this book to anyone who I thought could benefit from it. The text sits at the intersection of computer science and computational biology. It centres around the observation made by the author and others that often in CS, one has to manipulate strings of text, which are just sequences of text. While in computational biology, a recurrent theme is how to deal with sequences of molecules. These might be in a DNA sample or in a protein. <br /> <br />Surprisingly, from this simple observation, Gusfield manages to gather together considerable material. Over the decades, computing has accrued many algorithms for text string processing. The book's merit is in presenting those which are also applicable in bioinfomatics. The level of treatment is sophisticated, from the computing vantage. Enough so that perhaps the typical geneticist might not be able to easily follow the narrative. But a researcher with a strong background in both fields might be able to benefit. | |
 | ||
| By: Volker Grimm, Steven F. Railsback ISBN: 069109666X Publisher: Princeton University Press Release Date: 05 July, 2005 Bioscience book rank: 305020 | After having read this book from cover to cover - I currently consider it a "Must Have" on the bookshelf of anyone who is serious about ecological modelling or complex systems. The book is comprehensive, clear, honest, deep and enlightening. I found it a very clear, useful and inspiring introduction (what's) and guide (how to) to individual based modelling and ecology. 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. | |
 | ||
| By: O. Diekmann, J. A. P. Heesterbeek ISBN: 0471492418 Publisher: Wiley Release Date: 25 May, 2000 Bioscience book rank: 477675 | I purchased this book partially because one review proposed that this text would serve as a good, teach-yourself introduction to mathematical modeling of infectious disease (I.D.). I'm currently in a master's-level class on I.D. modeling which has no specific text requirement, and having only a so-so math background and little knowledge of model construction, I thought a self-teach book would be nice. <br /> <br />Simply said, this book is not for those who stumbled through calculus 1. In fact, unless you're quite well-to-do in the math department, you'll find much of this text either very challenging or impenetrable. The worked-out problems are a very nice touch--one that many authors would do well to note--but the high-level math is too much for this budding epidemiologist. | |
 | ||
| By: Fabio Dercole, Sergio Rinaldi ISBN: 0691120064 Publisher: Princeton University Press Release Date: 06 March, 2008 Bioscience book rank: 180414 | ||
 | ||
| By: Darren J. Wilkinson ISBN: 1584885408 Publisher: Chapman & Hall/CRC Release Date: 18 April, 2006 Bioscience book rank: 157943 | This is one of several recent books with system biology in the title, but first book with emphasis on statistics and stochastic approach. The book discusses some simple math models for biological systems, mostly biochemical models. Main focus is on statistical issues in differentail modeling, those as discussed in the nice Bower and Bolouri (2000) book [[ASIN:0262524236 Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)]], and the strong point is the use of Bayesian Markov chain Monte Carlo method for stochastic modeling. Weakness is that, the systems considered are too low-dimensional, and there is a long way toward real system biology issues, which will likely be much more complex, and involve potentially many high-dimensional interactions. Overall, it is simply a very straightforward introductory book, lacks depth and breadth. However, it should serve as a good starting point for people who want to learn something about stat and probabilistic methods relevant to bios system modelling. | |
 | ||
| By: Richard C. Deonier, Simon Tavaré, Michael S. Waterman ISBN: 0387987851 Publisher: Springer Release Date: August, 2005 Bioscience book rank: 164459 | This textbook is used as the main text for one of my graduate courses. It is a well written book and contains a plethora of information. The problem is that I find myself constantly re-reading sections and walking through examples to thoroughly understand them. Nothing seems to click the first time I read through the information (or sometimes even second, third, etc.).
<br />
<br />This is my first time taking any coursework in the bioinformatics field so perhaps it is simply because this material is new to me, but I found this book fairly difficult to read. I had to supplement it with other books, wikipedia entries, etc. to be able to understand many of the terms (which this book fails to define).
<br />
<br />If you're willing to put forth the effort of filling in the gaps, then this is a great book. If you already have a strong background in computer science and biology then this is likely an excellent book for reference material, or to expand you knowledge in an already familiar area.
<br />
<br />Also note that there is a large amount of discussion of probability in this area of study. You may wish to brush up on your skills in probability prior to reading this. This textbook was based on the authors' instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master's, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry. <br /> <br />This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or 'machine' utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species. <br /> <br />The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computational biology to fully understand them, the authors include examples of computations using R. This volume is described as a "roll up your sleeves and get dirty" introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for intellectual growth as new experimental approaches lead to new computational tools. <br /> <br />One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics --- the 98% being processed in R language. It isn't intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher's answer book had been appended. <br /> <br />The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data. <br /> <br />Leonard C. Silvern <br />Systems Engineering Laboratories <br />Clarkdale, AZ <br /> <br /> <br /> <br /> <br /> | |
 | ||
| By: Christopher Fall, Eric Marland, John Wagner, John Tyson ISBN: 0387953698 Publisher: Springer Release Date: 15 February, 2005 Bioscience book rank: 563019 | As a field of applied mathematics, computational biology has exploded in the last decade, and shows every sign of increasing in the next. This book overviews a few of the topics in the computational modeling of cells. I only read chapters 12 and 13 on molecular motors, and so my review will be confined to these. <p> Nanotechnology could be described as an up-and-coming field, but in the natural world one can find examples of this technology that surpass greatly what has been accomplished by human engineers. The authors begin their articles with a few examples of natural molecular machines, including the "rotary motors" DNA helicase and bacteriophage, and the "linear motor" kinesin, the latter they refer to as a "walking enzyme". Important in the modeling of all these is the theory of stochastic processes in the guise of Brownian motion, which the authors hold is the key to understanding the mechanics of proteins. In chapter 12 they give a detailed overview of the mathematical modeling of protein dynamics, followed in chapter 13 by an illustration of the mathematical formalism in the bacterial flagellar motor, a polymerization ratchet, and a motor governing ATP synthase. <p> To the authors a molecular motor is an entity that converts chemical energy into mechanical force. The production of mechanical force though may involve intermediate steps of energy transduction, all these involving the release of free energy during binding events. But due to their size, molecular motors are subjected to thermal fluctuations, and thus to model their motion accurately requires the theory of stochastic processes. Thus the authors begin a study of stochastic processes, restricting their attention to ones that satisfy the Markov property. Starting with a discrete model of protein motion as a simple random walk, the authors show that the variance of the motion grows linearly with time, which is a sign of diffusive motion. The partial differential equation satisfied by the probability distribution function, in the continuous limit where the space and time scales are large enough, is left to the reader to derive as an exercise. <p> The authors then consider polymer growth as another example of a stochastic process, a kind of hybrid one in that it involves both discrete and continuous random variables, the position of the polymer being continuous, while the number of monomers in the polymer is discrete. The authors derive an ordinary differential equation for the probability of there being exactly n polymers at a particular time. From this they show how to obtain sample paths for polymer growth and give a brief discussion on the statistics of polymer growth. <p> Attention is then turned to the modeling of molecular motions, with the first example being the Brownian motion of proteins in aqueous solutions. The (stochastic) Langevin equation is given for the motion of the protein, both with and without an external force acting on the protein. To find a numerical solution of this equation is straightforward, as the authors show. But they caution however that simulation of this solution on a computer is liable to introduce spurious results, and so they derive the Smoluchowski model, a somewhat different way of looking at random motion via the evolution of ensembles of paths. In this formulation the Brownian force is replaced by a diffusion term, and the external force is modeled by a drift term. <p> The authors then consider the modeling of chemical reactions, which supply the energy to the molecular motors. Because of the time scales involved in these reactions, a correct treatment of them would involve quantum mechanics, but the authors use the Smoluchowski model. The simple reaction model they consider involves a positive ion binding to negatively charged amino acid, and using as reaction coordinate the distance between the ion and the amino acid, study the free energy change as a function of the reaction coordinate. <p> The numerical simulation of the protein motion is then considered in much greater detail, using an algorithm that preserves detailed balance. This involves converting the problem to a Markov chain and a consideration of the boundary conditions, which the authors do for the case of periodic, reflecting, and absorbing. Euler's method is used to solve the resulting equations for the Markov chain, and after dealing with issues of stability and accuracy, the Crank-Nicolson method is used. The last few sections of the chapter are devoted to the physics of these solutions and the authors give some intuitive feel for the entropic factors and energy balance on a protein motor. <p> In the last chapter of the book, the considerations in chapter 12 are applied to concrete molecular motors. The first one examined is a model for switching in a bacterial flagellar motor, which involves the protein CheY as a signaling pathway. The binding of CheY to the motor is modeled as a two-state process, with the binding site being either empty or occupied. The resulting set of coupled differential equations for the probabilities is solved for when the concentration of CheY is constant. An expression for the change in free energy is obtained, and the authors give a discussion of the physics in the light of what was done in the last chapter. The switching rate is computed, along with the mean first passage time. <p> Some other examples of molecular motors are also discussed, including the flashing racket, the polymerization ratchet, and a simplified model of the ion-driven F0 motor of ATP synthase. This latter motor is fascinating, since it describes the electrochemical energy involved in mitochondria for the production of ATP. The authors do a nice job of showing how the techniques of chapter 12 are used to solve this model, and also give an analytical solution for a certain limiting case. | |
Related books in this category:
bioinformatics, computational biology, sequence analysis, protein structure, data mining, biostatistics
Main book index: all categories
|
| ||||||||