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Modeling Biological Systems [recurso electrónico] : Principles and Applications / by James W. Haefner.

Por: Colaborador(es): Tipo de material: TextoTextoEditor: Boston, MA : Springer US, 2005Edición: Second EditionDescripción: XVI, 480p. 180 illus. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • recurso en línea
ISBN:
  • 9780387250120
  • 99780387250120
Tema(s): Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD:
  • 570 23
Recursos en línea:
Contenidos:
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.
En: Springer eBooksResumen: 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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Holdings
Item type Current library Collection Call number Status Date due Barcode
Libros electrónicos Libros electrónicos CICY Libro electrónico Libro electrónico 570 (Browse shelf(Opens below)) Available

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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