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Modelling Methodology for Physiology and Medicine - 2nd Edition - ISBN: 9780124115576, 9780124095250

Modelling Methodology for Physiology and Medicine

2nd Edition

Authors: Ewart Carson Claudio Cobelli
eBook ISBN: 9780124095250
Hardcover ISBN: 9780124115576
Imprint: Elsevier
Published Date: 5th December 2013
Page Count: 588
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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

6.7 Conclusions


7. Population Modelling

7.1 Introduction

7.2 Naïve Data Approaches: Naïve Average and Naïve Pooled Data

7.3 Two-Stage Approaches: Standard, Global, and Iterative Two-Stage

7.4 Nonlinear Mixed-Effects Modelling

7.5 Covariate Models in Nonlinear Mixed-Effects Models


8. Systems Biology

8.1 Introduction

8.2 Modelling the System: ODE Models

8.3 Modelling the Data: Statistical Models

8.4 Applications

8.5 Conclusions



9. Reverse Engineering of High-Throughput Genomic and Genetic Data


9.1 Introduction

9.2 Reverse Engineering Transcriptional Data

9.3 Reverse Engineering Genetic Genomics Data

9.4 Conclusion


10. Tracer Experiment Design for Metabolic Fluxes Estimation in Steady and Nonsteady State


10.1 Introduction

10.2 Fundamentals

10.3 Accessible Pool and System Fluxes

10.4 The Tracer Probe

10.5 Estimation of Tracee Fluxes in Steady State

10.6 Estimation of Nonsteady-State Fluxes

10.7 Conclusion


11. Stochastic Models of Physiology


11.1 Introduction

11.2 Randomness and Probability

11.3 Probability Distributions and Stochastic Processes

11.4 The Law of Large Numbers and Limit Theorems

11.5 Analysis of Stochastic Associations: Correlation and Regression

11.6 Distances, Mean Comparisons, Clustering, and Principal Components

11.7 Markov Chains

11.8 State Estimation for Discrete-Time Linear Systems: Kalman Filtering

11.9 Conclusion


12. Probabilistic Modelling with Bayesian Networks


12.1 Introduction

12.2 Theoretical Foundations

12.3 Algorithms

12.4 Examples

12.5 Conclusions and Future Perspectives


13. Mathematical Modelling of Pulmonary Gas Exchange

13.1 Standard Equations Used to Describe Gas Transport in the Lungs

13.2 Models of Diffusion Limitation

13.3 Models of Ventilation–Perfusion Mismatch

13.4 Application of Mathematical Models of Ventilation, Perfusion, and Diffusion


Appendix A—Glossary

Appendix B—Calculations Necessary to Convert Inspired Gas at ATPD to BTPS

14. Mathematical Models for Computational Neuroscience

14.1 Introduction

14.2 Models of Individual Neural Units

14.3 Networks of Neurons

14.4 Conclusions


15. Insulin Modelling

15.1 Dynamics of Insulin Secretion

15.2 Cellular Modelling of Beta-Cell Function

15.3 Whole-Body Modelling of Beta-Cell Function

15.4 Multiscale Modelling of Insulin Secretion

15.5 Conclusion


16. Glucose Modelling

16.1 Introduction

16.2 Oral Glucose Minimal Models

16.3 Oral Glucose Maximal Models

16.4 Conclusion


17. Blood–Tissue Exchange Modelling

17.1 Introduction

17.2 Theory and Experimental Approaches

17.3 Models of Blood–Tissue Exchange

17.4 Identification of Blood–Tissue Exchange Models

17.5 Applications

17.6 Conclusions


18. Physiological Modelling of Positron Emission Tomography Images

18.1 Introduction

18.2 Modelling Strategies

18.3 PET Measurement Error

18.4 Models of Regional Glucose Metabolism

18.5 Models of [15O]H2O Kinetics to Assess Blood Flow

18.6 Models of the Ligand–Receptor System

18.7 The Way Forward

18.8 Conclusion


19. Tumor Growth Modelling for Drug Development

19.1 Introduction

19.2 R&D Cycle Time: From Discovery to Launch

19.3 Preclinical Development in Oncology

19.4 A Preclinical Tumor Growth Inhibition Model

19.5 Mathematical Analysis of the TGI Model

19.6 Model Identification and its Applications

19.7 Combined Administration of Drugs

19.8 Model-Based Clinical Dose Prediction

19.9 Conclusions


20. Computational Modelling of Cardiac Biomechanics

20.1 Introduction

20.2 Modelling of Ventricular Biomechanics

20.3 Models Assessing Ventricular Global Function

20.4 Image-Based Assessment of Ventricular Biomechanics

20.5 Multiphysics Patient-Specific Models of the Left Ventricle

20.6 3D Patient-Specific Heart Valve Modelling: Early Approaches

20.7 3D Patient-Specific Heart Valve Modelling: Recent Advances

20.8 Conclusion


21. Downstream from the Heart Left Ventricle: Aortic Impedance Interpretation by Lumped and Tube-Load Models

21.1 Introduction

21.2 Lumped-Parameter Models

21.3 Tube-Load Models

21.4 Conclusion


22. Finite Element Modelling in Musculoskeletal Biomechanics

22.1 Introduction

22.2 Background

22.3 Finite Element Modelling in Biomechanics

22.4 The Modelling Process

22.5 Postprocessing

22.6 Validation

22.7 Case Study: FEA Foot Biomechanics

22.8 Conclusion



23. Modelling for Synthetic Biology

23.1 Background

23.2 Models of Genetic Circuits

23.3 Experimental Measurements for Parameter Identification

23.4 Conclusion



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© Elsevier 2013
5th December 2013
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About the Authors

Ewart Carson

Ewart Carson is Emeritus Professor of Systems Science in the School of Mathematics, Computer Science and Engineering at City, University of London. Educated at the University of St Andrews in Scotland and City University London, he holds a PhD in Systems Science and a DSc in Measurement and Information in Medicine. He holds Honorary Membership of the Royal College of Physicians (London) , a Life Fellowship of the IEEE, Fellowships of the International Academy of Medical and Biological Engineering and the American Institute of Medical and Biological Engineers, and is a Foundation Fellow of the European Alliance for Medical and Biological Engineering and Science Publications include 13 authored and edited books and more than 300 journal papers and chapters. Areas of research interest and expertise include: modelling in physiology and medicine; modelling methodology for health resource management; clinical decision support systems; evaluation methodologies with particular application in telemedicine; and integrated policy modelling for ICT enhanced public healthcare. As a systems scientist, all this research is undertaken within a clear systemic framework.

Affiliations and Expertise

Emeritus Professor of Systems Science in the School of Mathematics, Computer Science and Engineering at City University of London, UK

Claudio Cobelli

Claudio Cobelli received a Doctoral degree (Laurea) in Electrical Engineering in 1970 from the University of Padova, Padova, Italy. From 1970 to 1980, he was a Research Fellow of the Institute of System Science and Biomedical Engineering, National Research Council, Padova, Italy. From 1973 to 1975 and 1975 to 1981, he was Associate Professor of Biological Systems at the University of Florence and Associate Professor of Biomedical Engineering at the University of Padova, respectively. In 1981, he becomes Full Professor of Biomedical Engineering at University of Padova. From 2000 to 2009, he has been Chairman of the Graduate Program in Biomedical Engineering. From 2000 to 2011, he has been Chairman of the Ph.D. Program in Bioengineering at the University of Padova. His main research activity is in the field of modeling and identification of physiological systems, especially metabolic systems. His research is currently supported by NIH, JDRF and European Comunity. He has published 450 papers in internationally refereed journals, co-author of 8 books and holds 11 patents. He is currently Associate Editor of IEEE Transaction on Biomedical Engineering and Journal of Diabetes Science & Technology. He is on the Editorial Board of Diabetes and Diabetes Technology &Therapeutics. Dr.Cobelli has been Chairman (1999-2004) of the Italian Biomedical Engineering Group, Chairman (1990-1993 & 1993-1996) of IFAC TC on Modeling and Control of Biomedical Systems and member of the IEEE EMBS AdCom Member (2008-2009). He has been a member of the Gruppo di Esperti della Valutazione (GEV), Area 09, of the Agenzia Nazionale per la Valutazione del Sistema Universitario e della Ricerca (ANVUR) for the period 2011-2013. He is President of the Organo di Indirizzo of the Azienda Ospedaliera Universita' di Trieste In 2010 he received the Diabetes Technology Artificial Pancreas Research Award. He is Fellow of IEEE, BMES and EAMBES.

Affiliations and Expertise

Department of Information Engineering, Universita di Padova, Italy

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