Modelling Methodology for Physiology and Medicine
- 2nd Edition - October 30, 2018
- Latest edition
- Authors: Ewart Carson, Claudio Cobelli
- Language: English
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… Read more
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.
- 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).
Preface
Preface to the Second Edition
List of Contributors
1. An Introduction to Modelling Methodology
Abstract
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
Reference
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
References
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
References
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
References
5. Parameter Estimation
5.1 Problem Statement
5.2 Fisherian Parameter Estimation Approaches
5.3 Bayesian Parameter Estimation Approaches
5.4 Conclusions
References
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
References
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
References
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
Acknowledgments
References
9. Reverse Engineering of High-Throughput Genomic and Genetic Data
Abstract
9.1 Introduction
9.2 Reverse Engineering Transcriptional Data
9.3 Reverse Engineering Genetic Genomics Data
9.4 Conclusion
References
10. Tracer Experiment Design for Metabolic Fluxes Estimation in Steady and Nonsteady State
Abstract
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
References
11. Stochastic Models of Physiology
Abstract
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
References
12. Probabilistic Modelling with Bayesian Networks
Abstract
12.1 Introduction
12.2 Theoretical Foundations
12.3 Algorithms
12.4 Examples
12.5 Conclusions and Future Perspectives
References
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
References
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
References
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
References
16. Glucose Modelling
16.1 Introduction
16.2 Oral Glucose Minimal Models
16.3 Oral Glucose Maximal Models
16.4 Conclusion
References
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
References
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
References
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
References
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
References
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
References
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
Acknowledgment
References
23. Modelling for Synthetic Biology
23.1 Background
23.2 Models of Genetic Circuits
23.3 Experimental Measurements for Parameter Identification
23.4 Conclusion
References
- Edition: 2
- Latest edition
- Published: October 30, 2018
- Language: English
EC
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, UKCC
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, ItalyRead Modelling Methodology for Physiology and Medicine on ScienceDirect