This extensively revised second edition describes the essentials of creating and analyzing mathematical and computer simulation models. 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. The new edition extensively updates many of these topics, and is more rigorous and mathematical than the competitive titles, which are intended for a less sophisticated reader.

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

Klappentext

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.

Inhalt
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.