- 384 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Introduction to Modeling in Physiology and Medicine
About this book
Introduction to Modeling in Physiology and Medicine, Second Edition, develops a clear understanding of the fundamental principles of good modeling methodology. Sections show how to create valid mathematical models that are fit for a range of purposes. These models are supported by detailed explanation, extensive case studies, examples and applications. This updated edition includes clearer guidance on the mathematical prerequisites needed to achieve the maximum benefit from the material, a greater detail regarding basic approaches to modeling, and discussions on non-linear and stochastic modeling. The range of case study material has been substantially extended, with examples drawn from recent research experience.Key examples include a cellular model of insulin secretion and its extension to the whole-body level, a model of insulin action during a meal/oral glucose tolerance test, a large-scale simulation model of type 1 diabetes and its use in in silico clinical trials and drug trials.- Covers the underlying principles of good quantitative modeling methodology, with applied biomedical engineering and bioscience examples to ensure relevance to students, current research and clinical practice- Includes modeling data, modeling systems, linear and non-linear systems, model identification, parametric and non-parametric models, and model validation- Presents clear, step-by-step working plus examples and extensive case studies that relate concepts to real world applications- Provides end-of-chapter exercises and assignments to reinforce learning
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Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface to the second edition
- Preface to the first edition
- 1. Introduction
- 2. Physiological complexity and the need for models
- 3. Models and the modeling process
- 4. Modeling the data
- 5. Modeling the system
- 6. Model identification
- 7. Parametric modeling—the identifiability problem
- 8. Parametric models—the estimation problem
- 9. Nonparametric models—signal estimation
- 10. Model validation
- 11. Case studies
- References
- Index