Main description:

Shows the newest developments in the field of multi-parametric model predictive control and optimization and their application for drug delivery systems

This book is based on the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project, which was created to derive intelligent computer model-based systems for optimization of biomedical drug delivery systems in the cases of diabetes, anaesthesia, and blood cancer. These systems can ensure reliable and fast calculation of the optimal drug dosage without the need for an online computer-while taking into account the specifics and constraints of the patient model, flexibility to adapt to changing patient characteristics and incorporation of the physician's performance criteria, and maintaining the safety of the patients.

Modelling Optimization and Control of Biomedical Systems covers: mathematical modelling of drug delivery systems; model analysis, parameter estimation, and approximation; optimization and control; sensitivity analysis & model reduction; multi-parametric programming and model predictive control; estimation techniques; physiologically-based patient model; control design for volatile anaesthesia; multiparametric model based approach to intravenous anaesthesia; hybrid model predictive control strategies; Type I Diabetes Mellitus; in vitro and in silico block of the integrated platform for the study of leukaemia; chemotherapy treatment as a process systems application; and more.

Introduces readers to the Modelling, Control and Optimization of Biomedical Systems (MOBILE) project
Presents in detail the theoretical background, computational tools, and methods that are used in all the different biomedical systems
Teaches the theory for multi-parametric mixed-integer programming and explicit optimal control of volatile anaesthesia
Provides an overview of the framework for modelling, optimization, and control of biomedical systems

This book will appeal to students, researchers, and scientists working on the modelling, control, and optimization of biomedical systems and to those involved in cancer treatment, anaesthsia, and drug delivery systems.

Contents:

List of contributors Preface CHAPTER 1 1.1 Mathematical Modelling of Drug Delivery Systems 1.2 Model analysis, parameter estimation and approximation 1.3 Optimisation and control References CHAPTER 2 2.1 Introduction 2.2 Sensitivity Analysis & Model Reduction 2.3. Multi-Parametric Programming and Model Predictive Control 2.4. Estimation techniques 2.5. Explicit Hybrid Control References CHAPTER 3 3.1 Introduction 3.2 Physiologically based patient model 3.3 Model analysis 3.4 Control Design for Volatile Anaesthesia Conclusions Appendix Variable lists References CHAPTER 4 4.1 A Multiparametric Model Based Approach to Intravenous Anaesthesia 4.2 Simultaneous Estimation and Advanced Control 4.3 Hybrid Model Predictive Control Strategies 4.4 Conclusions References CHAPTER 5 5.a Type I Diabetes Mellitus: Modelling, Model Analysis and Optimisation 5.a.1 Introduction: Type 1 Diabetes Mellitus 5.a.2 Modelling the Glucoregulatory System 5.a.3 Physiologically based Compartmental Model 5.a.4 Model Analysis 5.a.5 Simulation Results 5.a.6 Dynamic Optimisation 5.b Type I Diabetes Mellitus: Glucose Regulation 5.b.1 Glucose-Insulin System: Typical Control Problem 5.b.2 Model Predictive Control Framework 5.b.3 Control Design 5.b.4 Simulation Results 5.b.5 Explicit MPC APPENDIX 5. A APPENDIX 5. B APPENDIX 5. C References CHAPTER 6 6.1 Towards a personalised treatment for Leukaemia: From in vivo to in vitro and in silico. 6.2 In vitro block of the integrated platform for the study of leukaemia. 6.3 In silico block of the integrated platform for the study of leukaemia. 6.4 Bridging the gap between In vitro-In silico. References CHAPTER 7 7.1 Description of biomedical system 7.2 Experimental part 7.3 Cellular Biomarkers for monitoring leukaemia in vitro 7.4 From in vitro to in silico References CHAPTER 8 8.1 Introduction 8.2 Chemotherapy treatment as a process systems application 8.3 Analysis of a patient case study 8.4 Conclusions References Appendix 8.A: Mathematical model Appendix 8.B: Patient Data Index