Modelling Methodology for Physiology and Medicine, Second Edition, offers a unique approach and an unprecedented range of coverage of the state-of-the-art, advanced modeling methodology that is widely applicable to physiology and medicine. The second edition, which is completely updated and expanded, opens with a clear and integrated treatment of advanced methodology for developing mathematical models of physiology and medical systems. Readers are then shown how to apply this methodology beneficially to real-world problems in physiology and medicine, such as circulation and respiration.

The focus of Modelling Methodology for Physiology and Medicine, Second Edition, is the methodology that underpins good modeling practice. It builds upon the idea of an integrated methodology for the development and testing of mathematical models. It covers many specific areas of methodology in which important advances have taken place over recent years and illustrates the application of good methodological practice in key areas of physiology and medicine. It builds on work that the editors have carried out over the past 30 years, working in cooperation with leading practitioners in the field.

Key Features

  • Builds upon and enhances the reader's existing knowledge of modeling methodology and practice
  • Editors are internationally renowned leaders in their respective fields
  • Provides an understanding of modeling methodologies that can address real problems in physiology and medicine and achieve results that are beneficial either in advancing research or in providing solutions to clinical problems


Practitioners, researchers, and students in the field of modelling with specialties in physiology and medicine (drawn from the related fields of engineering, informatics, computing, medicine, and physiology).

Table of Contents


Preface to the Second Edition

List of Contributors

1. An Introduction to Modelling Methodology


1.1 Introduction

1.2 The Need for Models

1.3 Approaches to Modelling

1.4 Simulation

1.5 Model Identification

1.6 Model Validation


2. Control in Physiology and Medicine

2.1 Introduction

2.2 Modelling for Control System Design and Analysis

2.3 Block Diagram Analysis

2.4 Proportional-Integral-Derivative Control

2.5 Model Predictive Control

2.6 Other Control Algorithms

2.7 Application Examples

2.8 Summary


3. Deconvolution

3.1 Problem Statement

3.2 Difficulty of the Deconvolution Problem

3.3 The Regularization Method

3.4 Other Deconvolution Methods

3.5 Conclusions


4. Structural Identifiability of Biological and Physiological Systems

4.1 Introduction

4.2 Background and Definitions

4.3 Identifiability and Differential Algebra

4.4 The Question of Initial Conditions

4.5 Identifiability of Some Nonpolynomial Models

4.6 A Case Study

4.7 Conclusion


5. Parameter Estimation

5.1 Problem Statement

5.2 Fisherian Parameter Estimation Approaches

5.3 Bayesian Parameter Estimation Approaches

5.4 Conclusions


6. New Trends in Nonparametric Linear System Identification

6.1 Introduction

6.2 System Identification Problem

6.3 The Classical Approach to System Identification

6.4 Limitations of the Classical Approach to System Identification: Assessment of Cerebral Hemodynamics Using MRI

6.5 The Nonparametric Gaussian Regression Approach to System Identification

6.6 Assessment of Cerebral Hemodynamics Using the Stable Spline Estimator<


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© 2014
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