Modelling Methodology for Physiology and Medicine

Modelling Methodology for Physiology and Medicine

2nd Edition - December 5, 2013

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  • Authors: Ewart Carson, Claudio Cobelli
  • eBook ISBN: 9780124095250
  • Hardcover ISBN: 9780124115576

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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

    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


Product details

  • No. of pages: 588
  • Language: English
  • Copyright: © Elsevier 2013
  • Published: December 5, 2013
  • Imprint: Elsevier
  • eBook ISBN: 9780124095250
  • Hardcover ISBN: 9780124115576

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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