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Modeling Biological Systems
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Modeling Biological Systems : Principles and Applications

Softcover reprint of the original 1st ed. 1996

Book Details

Format Paperback / Softback
ISBN-10 1461368472
ISBN-13 9781461368472
Edition Softcover reprint of the original 1st ed. 1996
Publisher Springer-Verlag New York Inc.
Imprint Springer-Verlag New York Inc.
Country of Manufacture US
Country of Publication GB
Publication Date Oct 16th, 2012
Print length 473 Pages
Ksh 23,400.00
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This book is intended as a text for a first course on creating and analyzing computer simulation models of biological systems. I have stressed the relationship between the models and the biology over mathematical analysis in order to give the reader a sense that mathematical models really are useful to biologists.
This book is intended as a text for a first course on creating and analyzing computer simulation models of biological systems. The expected audience for this book are students wishing to use dynamic models to interpret real data mueh as they would use standard statistical techniques. It is meant to provide both the essential principles as well as the details and equa­ tions applicable to a few particular systems and subdisciplines. Biological systems, however, encompass a vast, diverse array of topics and problems. This book discusses only a select number of these that I have found to be useful and interesting to biologists just beginning their appreciation of computer simulation. The examples chosen span classical mathematical models of well-studied systems to state-of-the-art topics such as cellular automata and artificial life. I have stressed the relationship between the models and the biology over mathematical analysis in order to give the reader a sense that mathematical models really are useful to biologists. In this light, I have sought examples that address fundamental and, I think, interesting biological questions. Almost all of the models are directly COIIl­ pared to quantitative data to provide at least a partial demonstration that some biological models can accurately predict.

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