Record Display for the EPA National Library Catalog
RECORD NUMBER: 2241 OF 4003Main Title | Modeling Biological Systems Principles and Applications / [electronic resource] : | ||||
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Type | EBOOK | ||||
Author | Haefner, James W. | ||||
Publisher | Springer US, | ||||
Year Published | 2005 | ||||
Call Number | QH301-705 | ||||
ISBN | 9780387250120 | ||||
Subjects | Life sciences ; Bioinformatics ; Biology--Data processing ; Ecology ; Zoology | ||||
Internet Access |
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Edition | Second Edition. | ||||
Collation | XI, 475 p. online resource. | ||||
Notes | Due to license restrictions, this resource is available to EPA employees and authorized contractors only |
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Contents Notes | Principles -- Models of Systems -- The Modeling Process -- Qualitative Model Formulation -- Quantitative Model Formulation: I -- Quantitative Model Formulation: II -- Numerical Techniques -- Parameter Estimation -- Model Validation -- Model Analysis: Uncertainty and Behavior -- Stochastic Models -- Applications -- Photosynthesis and Plant Growth -- Hormonal Control in Mammals -- Populations and Individuals -- Chemostats -- Diseases -- Spatial Patterns and Processes -- Scaling Models -- Chaos in Biology -- Cellular Automata and Recursive Growth -- Evolutionary Computation. 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. |