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Mathematics Computational Biology: A Statistical Mechanics Perspective (Chapman

Posted on 2010-04-15




Name:Mathematics Computational Biology: A Statistical Mechanics Perspective (Chapman
ASIN/ISBN:1584885564
Author:Ralf Blossey
Publisher:Chapman & Hall/CRC (2006)
Pages:Hardcover, 276 pages
   Mathematics Computational Biology: A Statistical Mechanics Perspective (Chapman

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Author: Ralf Blossey


Publisher: Chapman & Hall/CRC (2006)


Binding: Hardcover, 276 pages


pricer: $84.95


ISBN-10: 1584885564


editorialreviews

Quantitative methods have a particular knack for improving any field they touch. For biology, computational techniques have led to enormous strides in our understanding of biological systems, but there is still vast territory to cover. Statistical physics especially holds great potential for elucidating the structural-functional relationships in biomolecules, as well as their static and dynamic properties. Breaking New Ground Computational Biology: A Statistical Mechanics Perspective is the first book dedicated to the interface between statistical physics and bioinformatics. Introducing both equilibrium and nonequilibrium statistical mechanics in a manner tailored to computational biologists, the author applies these methods to understand and model the properties of various biomolecules and biological networks at the systems level. Unique Vision, Novel Approach Blossey combines his enthusiasm for uniting the fields of physics and computational biology with his considerable experience, knowledge, and gift for teaching. He uses numerous examples and tasks to illustrate and test understanding of the concepts, and he supplies a detailed keyword list for easy navigation and comprehension. His approach takes full advantage of the latest tools in statistical physics and computer science to build a strong set of tools for confronting new challenges in computational biology. Making the concepts crystal clear without sacrificing mathematical rigor, Computational Biology: A Statistical Mechanics Perspective is the perfect tool to broaden your skills in computational biology.




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