Introduction to Modeling in Physiology and Medicine 2nd edition by Claudio Cobelli,Ewart Carson 9780128158050 0128158050

1. Introduction

Abstract

1.1 Introduction

1.2 The book in context

1.3 The major ingredients

1.4 Readership and prerequisites

1.5 Organization of the book

2. Physiological complexity and the need for models

Abstract

2.1 Introduction

2.2 Complexity

2.3 System dynamics

2.4 Feedback

2.5 Control in physiological systems

2.6 Hierarchy

2.7 Redundancy

2.8 Function and behavior and their measurement

2.9 Challenges to understanding

2.10 Exercises and assignment questions

3. Models and the modeling process

Abstract

3.1 Introduction

3.2 What is a model?

3.3 Why model? The purpose of modeling

3.4 How do we model? The modeling process

3.5 Model formulation

3.6 Model identification

3.7 Model validation

3.8 Model simulation

3.9 Summary

3.10 Exercises and assignment questions

4. Modeling the data

Abstract

4.1 Introduction

4.2 The basis of data modeling

4.3 The why and when of data models

4.4 Approaches to data modeling

4.5 Modeling a single variable occurring spontaneously

4.6 Modeling a single variable in response to a perturbation

4.7 Two variables causally related

4.8 Input/output modeling for control

4.9 Input/output modeling: impulse response and deconvolution

4.10 Summary

4.11 Exercises and assignment questions

5. Modeling the system

Abstract

5.1 Introduction

5.2 Static models

5.3 Linear modeling

5.4 Distributed modeling

5.5 Nonlinear modeling

5.6 Time-varying modeling

5.7 Stochastic modeling

5.8 Summary

5.9 Exercises and assignment questions

6. Model identification

Abstract

6.1 Introduction

6.2 Data for identification

6.3 Errors

6.4 The way forward

6.5 Summary

6.6 Exercises and assignment questions

7. Parametric modeling—the identifiability problem

Abstract

7.1 Introduction

7.2 Some examples

7.3 Definitions

7.4 Linear models: the transfer function method

7.5 Nonlinear models: the Taylor series expansion method

7.6 Qualitative experimental design

7.7 Summary

7.8 Exercises and assignment questions

8. Parametric models—the estimation problem

Abstract

8.1 Introduction

8.2 Linear and nonlinear parameters

8.3 Regression: basic concepts

8.4 Linear regression

8.5 Nonlinear regression

8.6 Tests for model order

8.7 Maximum likelihood estimation

8.8 Bayesian estimation

8.9 Optimal experimental design

8.10 Summary

8.11 Exercises and assignment questions

9. Nonparametric models—signal estimation

Abstract

9.1 Introduction

9.2 Why is deconvolution important?

9.3 The problem

9.4 Difficulty of the deconvolution problem

9.5 The regularization method

9.6 Summary

9.7 Exercises and assignment questions

10. Model validation

Abstract

10.1 Introduction

10.2 Model validation and the domain of validity

10.3 Validation strategies

10.4 Good practice in good modeling

10.5 Summary

10.6 Exercises and assignment questions

11. Case studies

Abstract

11.1 Case study 1: a sum of exponentials tracer disappearance model

11.2 Case study 2: blood flow modeling

11.3 Case study 3: cerebral glucose modeling

11.4 Case study 4: models of the ligand–receptor system

11.5 Case study 5: A model of insulin secretion from a stochastic cellular model to a whole-body model

11.6 Case study 6: a model of insulin control

11.7 Case study 7: a simulation model of the glucose-insulin system

11.8 Case study 8: the University of Virginia (UVA)/Padova type 1 simulator – in silico artificial pancreas, glucose sensors and new insulin trials

11.9 Case study 9: illustrations of Bayesian estimation

11.10 Postscript