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Modeling Biological Systems : Principles and Applications /

By: Haefner, James W [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Boston, MA : Springer US, 2005.Edition: Second Edition.Description: XI, 475 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387250120.Subject(s): Life sciences | Bioinformatics | Computational biology | Ecology | Zoology | Life Sciences | Life Sciences, general | Bioinformatics | Computer Appl. in Life Sciences | Ecology | ZoologyDDC classification: 570 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBK2375
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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.

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