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Modeling Biological Systems: Principles and Applications

Editat de James W. Haefner
en Limba Engleză Hardback – 31 iul 1996
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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Specificații

ISBN-13: 9780412042010
ISBN-10: 0412042010
Pagini: 473
Ilustrații: XIX, 473 p.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.79 kg
Ediția:Softcover reprint of the original 1st ed. 1996
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

I Principles.- 1 Models of Systems.- 2 The Modeling Process.- 3 Qualitative Model Formulation.- 4 Quantitative Model Formulation.- 5 Simulation Paradigms.- 6 Numerical Techniques.- 7 Parameter Estimation.- 8 Model Validation.- 9 Model Analysis.- 10 Stochastic Models.- II Applications.- 11 Photosynthesis and Plant Growth.- 12 Hormonal Control in Mammals.- 13 Populations and Individuals.- 14 Chemostats.- 15 Spatial Patterns and Processes.- 16 Scaling Models.- 17 Chaos in Biology.- 18 Cellular Automata and Recursive Growth.- 19 Evolutionary Computation.- 20 Complex Adaptive Systems.- References.

Recenzii

Throughout the book, the author aims to teach the concepts of mathematical modelling by example, allowing a full understanding of how and why mathematics is so important in biology. This is a superb introduction and complements the more technical literature... - The Biologist;
Covers a broad area with wit and enthusiasm, tempered with an appropriate degree of caution...a valuable introduction...a balanced selection of references makes this a handy source text for those seeking to learn more. - Journal of Animal Ecology;
The book will be useful for those readers who want to get a survey of the use of various types of models in biology. - Eur. Jnl. of Entomology, 1997;
This is an excellent book for the student who wants a mathematical model but does not know how to construct one - Journal of Experimental Marine Biology and Ecology.

Textul de pe ultima copertă

This extensively revised second edition of Modeling Biological Systems: Principles and Applications describes the essentials of creating and analyzing mathematical and computer simulation models for advanced undergraduates and graduate students. It offers a comprehensive understanding of the underlying principle, as well as details and equations applicable to a wide variety of biological systems and disciplines. Students will acquire from this text the tools necessary to produce their own models.
The text contains two major sections: Principles and Applications. The first section discusses the principles of biological systems with a thorough description of the essential modeling activities of formulation, implementation, validation, and analysis. These activities are illustrated by a set of example models taken from recent and classical literature, chosen for their breadth of coverage and current timeliness. The new edition updates extensively many of these topics, especially quantitative model formulation, validation and model discrimination using information theory measures and Bayesian probability, and stability analysis and non-dimensionalization.

Caracteristici

More rigorous and mathematical than the competitive titles, which are intended for a less sophisticated reader In an active and growing area, and the fact that it is a single author text is also an advantage