{"product_id":"modelling-optimization-and-control-of-biomedical-systems-9781118965597","title":"Modelling Optimization and Control of Biomedical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Contributors xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003ePart I 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Framework and Tools: A Framework for Modelling, Optimization and Control of Biomedical Systems 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEirini G. Velliou, Ioana Naşcu, Stamatina Zavitsanou, Eleni Pefani, Alexandra Krieger, Michael C.\u003c\/i\u003e \u003ci\u003eGeorgiadis, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Mathematical Modelling of Drug Delivery Systems 3\u003c\/p\u003e \u003cp\u003e1.1.1 Pharmacokinetic Modelling 3\u003c\/p\u003e \u003cp\u003e1.1.1.1 Compartmental Models 3\u003c\/p\u003e \u003cp\u003e1.1.1.2 Physiologically Based Pharmacokinetic Models 5\u003c\/p\u003e \u003cp\u003e1.1.2 Pharmacodynamic Modelling 5\u003c\/p\u003e \u003cp\u003e1.2 Model analysis, Parameter Estimation and Approximation 7\u003c\/p\u003e \u003cp\u003e1.2.1 Global Sensitivity Analysis 8\u003c\/p\u003e \u003cp\u003e1.2.2 Variability Analysis 8\u003c\/p\u003e \u003cp\u003e1.2.3 Parameter Estimation and Correlation 9\u003c\/p\u003e \u003cp\u003e1.3 Optimization and Control 9\u003c\/p\u003e \u003cp\u003eReferences 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Draft Computational Tools and Methods 13\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIoana Naşcu, Richard Oberdieck, Romain Lambert, Pedro Rivotti, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 13\u003c\/p\u003e \u003cp\u003e2.2 Sensitivity Analysis and Model Reduction 14\u003c\/p\u003e \u003cp\u003e2.2.1 Sensitivity Analysis 14\u003c\/p\u003e \u003cp\u003e2.2.1.1 Sobol’s Sensitivity Analysis 16\u003c\/p\u003e \u003cp\u003e2.2.1.2 High‐Dimensional Model Representation 17\u003c\/p\u003e \u003cp\u003e2.2.1.3 Group Method of Data Handling 18\u003c\/p\u003e \u003cp\u003e2.2.1.4 GMDH–HDMR 19\u003c\/p\u003e \u003cp\u003e2.2.2 Model Reduction 20\u003c\/p\u003e \u003cp\u003e2.2.2.1 Linear Model Order Reduction 21\u003c\/p\u003e \u003cp\u003e2.2.2.2 Nonlinear Model Reduction 22\u003c\/p\u003e \u003cp\u003e2.3 Multiparametric Programming and Model Predictive Control 24\u003c\/p\u003e \u003cp\u003e2.3.1 Dynamic Programming and Robust Control 28\u003c\/p\u003e \u003cp\u003e2.4 Estimation Techniques 33\u003c\/p\u003e \u003cp\u003e2.4.1 Kalman Filter 34\u003c\/p\u003e \u003cp\u003e2.4.1.1 Time Update (Prediction Step) 34\u003c\/p\u003e \u003cp\u003e2.4.1.2 Measurement Update (Correction Step) 34\u003c\/p\u003e \u003cp\u003e2.4.2 Moving Horizon Estimation 34\u003c\/p\u003e \u003cp\u003e2.5 Explicit Hybrid Control 39\u003c\/p\u003e \u003cp\u003e2.5.1 Multiparametric Mixed‐Integer Programming 40\u003c\/p\u003e \u003cp\u003e2.5.1.1 Problem and Solution Characterization 40\u003c\/p\u003e \u003cp\u003e2.5.1.2 Literature Review 42\u003c\/p\u003e \u003cp\u003e2.5.1.3 A General Framework for the Solution of mp‐MIQP Problems 48\u003c\/p\u003e \u003cp\u003e2.5.1.4 Detailed Analysis of the General Framework 50\u003c\/p\u003e \u003cp\u003e2.5.1.5 Description of an Exact Comparison Procedure 54\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Volatile Anaesthesia 67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlexandra Krieger, Ioana Naşcu, Nicki Panoskaltsis, Athanasios Mantalaris, Michael C. Georgiadis, and\u003c\/i\u003e \u003ci\u003eEfstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 67\u003c\/p\u003e \u003cp\u003e3.2 Physiologically Based Patient Model 69\u003c\/p\u003e \u003cp\u003e3.2.1 Pharmacokinetics 69\u003c\/p\u003e \u003cp\u003e3.2.1.1 Body Compartments 72\u003c\/p\u003e \u003cp\u003e3.2.1.2 Blood Volume 73\u003c\/p\u003e \u003cp\u003e3.2.1.3 Cardiac Output 73\u003c\/p\u003e \u003cp\u003e3.2.1.4 Lung Volume 74\u003c\/p\u003e \u003cp\u003e3.2.2 Pharmacodynamics 74\u003c\/p\u003e \u003cp\u003e3.2.3 Individualized Patient Variables and Parameters 74\u003c\/p\u003e \u003cp\u003e3.3 Model Analysis 75\u003c\/p\u003e \u003cp\u003e3.3.1 Uncertainty Identification via Patient Variability Analysis 75\u003c\/p\u003e \u003cp\u003e3.3.2 Global Sensitivity Analysis 77\u003c\/p\u003e \u003cp\u003e3.3.3 Correlation Analysis and Parameter Estimation 81\u003c\/p\u003e \u003cp\u003e3.3.4 Simulation Results 83\u003c\/p\u003e \u003cp\u003e3.4 Control Design for Volatile Anaesthesia 86\u003c\/p\u003e \u003cp\u003e3.4.1 State Estimation 87\u003c\/p\u003e \u003cp\u003e3.4.1.1 Model Linearization 88\u003c\/p\u003e \u003cp\u003e3.4.2 On‐Line Parameter Estimation 90\u003c\/p\u003e \u003cp\u003e3.4.2.1 Control and Algorithm Design 91\u003c\/p\u003e \u003cp\u003e3.4.2.2 Testing of the On‐Line Estimation Algorithm 93\u003c\/p\u003e \u003cp\u003e3.4.3 Case Study: Controller Testing for Isourane‐Based Anaesthesia 96\u003c\/p\u003e \u003cp\u003eConclusions 98\u003c\/p\u003e \u003cp\u003eAppendix 99\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Intravenous Anaesthesia 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIoana Naşcu, Alexandra Krieger, Romain Lambert, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 A Multiparametric Model‐based Approach to Intravenous Anaesthesia 103\u003c\/p\u003e \u003cp\u003e4.1.1 Introduction 103\u003c\/p\u003e \u003cp\u003e4.1.2 Patient Model 104\u003c\/p\u003e \u003cp\u003e4.1.3 Sensitivity Analysis 108\u003c\/p\u003e \u003cp\u003e4.1.4 Advanced Model‐based Control Strategies 110\u003c\/p\u003e \u003cp\u003e4.1.4.1 Extended Predictive Self‐adaptive Control (EPSAC) Strategy 111\u003c\/p\u003e \u003cp\u003e4.1.4.2 Multiparametric Strategy 111\u003c\/p\u003e \u003cp\u003e4.1.5 Control Design 112\u003c\/p\u003e \u003cp\u003e4.1.5.1 Case 1: EPSAC 115\u003c\/p\u003e \u003cp\u003e4.1.5.2 Case 2: mp‐MPC Without Nonlinearity Compensation 116\u003c\/p\u003e \u003cp\u003e4.1.5.3 Case 3: mp‐MPC With Nonlinear Compensation 117\u003c\/p\u003e \u003cp\u003e4.1.5.4 Case 4: mp‐MPC With Nonlinearity Compensation and Estimation 118\u003c\/p\u003e \u003cp\u003e4.1.6 Results 118\u003c\/p\u003e \u003cp\u003e4.1.6.1 Induction Phase 119\u003c\/p\u003e \u003cp\u003e4.1.6.2 Maintenance Phase 123\u003c\/p\u003e \u003cp\u003e4.1.6.3 Discussion 125\u003c\/p\u003e \u003cp\u003e4.2 Simultaneous Estimation and Advanced Control 130\u003c\/p\u003e \u003cp\u003e4.2.1 Introduction 130\u003c\/p\u003e \u003cp\u003e4.2.2 Multiparametric Moving Horizon Estimation (mp‐MHE) 130\u003c\/p\u003e \u003cp\u003e4.2.3 Simultaneous Estimation and mp‐MPC Strategy 132\u003c\/p\u003e \u003cp\u003e4.2.4 Results 134\u003c\/p\u003e \u003cp\u003e4.2.4.1 Induction Phase 135\u003c\/p\u003e \u003cp\u003e4.2.4.2 Maintenance Phase 138\u003c\/p\u003e \u003cp\u003e4.3 Hybrid Model Predictive Control Strategies 142\u003c\/p\u003e \u003cp\u003e4.3.1 Introduction 142\u003c\/p\u003e \u003cp\u003e4.3.2 Hybrid Patient Model Formulation 143\u003c\/p\u003e \u003cp\u003e4.3.3 Control Design 144\u003c\/p\u003e \u003cp\u003e4.3.3.1 Hybrid Formulation of the Control Problem: Intravenous Anaesthesia 144\u003c\/p\u003e \u003cp\u003e4.3.3.2 Robust Hybrid mp‐MPC Control Strategy: Offset Free 146\u003c\/p\u003e \u003cp\u003e4.3.3.3 Control Scheme 147\u003c\/p\u003e \u003cp\u003e4.3.4 Results 147\u003c\/p\u003e \u003cp\u003e4.3.4.1 No Offset Correction 147\u003c\/p\u003e \u003cp\u003e4.3.4.2 Offset Free 150\u003c\/p\u003e \u003cp\u003e4.3.5 Discussion 150\u003c\/p\u003e \u003cp\u003e4.4 Conclusions 153\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Part A: Type 1 Diabetes Mellitus: Modelling, Model Analysis and Optimization 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStamatina Zavitsanou, Athanasios Mantalaris, Michael C. Georgiadis, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.a Type 1 Diabetes Mellitus: Modelling, Model Analysis and Optimization 159\u003c\/p\u003e \u003cp\u003e5.a.1 Introduction: Type 1 Diabetes Mellitus 159\u003c\/p\u003e \u003cp\u003e5.a.1.1 The Concept of the Artificial Pancreas 160\u003c\/p\u003e \u003cp\u003e5.a.2 Modelling the Glucoregulatory System 162\u003c\/p\u003e \u003cp\u003e5.a.3 Physiologically Based Compartmental Model 162\u003c\/p\u003e \u003cp\u003e5.a.3.1 Endogenous Glucose Production (EGP) 167\u003c\/p\u003e \u003cp\u003e5.a.3.2 Rate of Glucose Appearance (Ra) 168\u003c\/p\u003e \u003cp\u003e5.a.3.3 Glucose Renal Excretion (Excretion) 168\u003c\/p\u003e \u003cp\u003e5.a.3.4 Glucose Diffusion in the Periphery 168\u003c\/p\u003e \u003cp\u003e5.a.3.5 Adaptation to the Individual Patient 169\u003c\/p\u003e \u003cp\u003e5.a.3.5.1 Total Blood Volume 169\u003c\/p\u003e \u003cp\u003e5.a.3.5.2 Cardiac Output 170\u003c\/p\u003e \u003cp\u003e5.a.3.5.3 Compartmental Volume 170\u003c\/p\u003e \u003cp\u003e5.a.3.5.4 Peripheral Interstitial Volume 171\u003c\/p\u003e \u003cp\u003e5.a.3.6 Insulin Kinetics 171\u003c\/p\u003e \u003cp\u003e5.a.4 Model Analysis 172\u003c\/p\u003e \u003cp\u003e5.a.4.1 Insulin Kinetics Model Selection 172\u003c\/p\u003e \u003cp\u003e5.a.4.2 Endogenous Glucose Production: Parameter Estimation 176\u003c\/p\u003e \u003cp\u003e5.a.4.3 Global Sensitivity Analysis 177\u003c\/p\u003e \u003cp\u003e5.a.4.3.1 Individual Model Parameters 178\u003c\/p\u003e \u003cp\u003e5.a.4.4 Parameter Estimation 182\u003c\/p\u003e \u003cp\u003e5.a.5 Simulation Results 183\u003c\/p\u003e \u003cp\u003e5.a.6 Dynamic Optimization 185\u003c\/p\u003e \u003cp\u003e5.a.6.1 Time Delays in the System 185\u003c\/p\u003e \u003cp\u003e5.a.6.2 Dynamic Optimization of Insulin Delivery 188\u003c\/p\u003e \u003cp\u003e5.a.6.3 Alternative Insulin Infusion 189\u003c\/p\u003e \u003cp\u003e5.a.6.4 Concluding Remarks 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart B: Type 1 Diabetes Mellitus: Glucose Regulation 192\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStamatina Zavitsanou, Athanasios Mantalaris, Michael C. Georgiadis, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.b Type 1 Diabetes Mellitus: Glucose Regulation 192\u003c\/p\u003e \u003cp\u003e5.b.1 Glucose–Insulin System: Typical Control Problem 192\u003c\/p\u003e \u003cp\u003e5.b.2 Model Predictive Control Framework 194\u003c\/p\u003e \u003cp\u003e5.b.2.1 “High‐Fidelity” Model 194\u003c\/p\u003e \u003cp\u003e5.b.2.2 The Approximate Model 195\u003c\/p\u003e \u003cp\u003e5.b.2.2.1 Linearization 195\u003c\/p\u003e \u003cp\u003e5.b.2.2.2 Physiologically Based Model Reduction 196\u003c\/p\u003e \u003cp\u003e5.b.3 Control Design 199\u003c\/p\u003e \u003cp\u003e5.b.3.1 Model Predictive Control 199\u003c\/p\u003e \u003cp\u003e5.b.3.2 Proposed Control Design 200\u003c\/p\u003e \u003cp\u003e5.b.3.3 Prediction Horizon 200\u003c\/p\u003e \u003cp\u003e5.b.3.4 Control Design 1: Predefined Meal Disturbance 202\u003c\/p\u003e \u003cp\u003e5.b.3.5 Control Design 2: Announced Meal Disturbance 202\u003c\/p\u003e \u003cp\u003e5.b.3.6 Control Design 3: Unknown Meal Disturbance 202\u003c\/p\u003e \u003cp\u003e5.b.3.7 Control Design 4: Unknown Meal Disturbance 204\u003c\/p\u003e \u003cp\u003e5.b.4 Simulation Results 204\u003c\/p\u003e \u003cp\u003e5.b.4.1 Predefined and Announced Disturbances 204\u003c\/p\u003e \u003cp\u003e5.b.4.2 Unknown Disturbance Rejection 204\u003c\/p\u003e \u003cp\u003e5.b.4.3 Variable Meal Time 207\u003c\/p\u003e \u003cp\u003e5.b.4.4 Concluding Remarks 207\u003c\/p\u003e \u003cp\u003e5.b.5 Explicit MPC 208\u003c\/p\u003e \u003cp\u003e5.b.5.1 Model Identification 209\u003c\/p\u003e \u003cp\u003e5.b.5.2 Concluding Remarks 211\u003c\/p\u003e \u003cp\u003eAppendix 5.1 212\u003c\/p\u003e \u003cp\u003eAppendix 5.2 215\u003c\/p\u003e \u003cp\u003eAppendix 5.3 215\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 An Integrated Platform for the Study of Leukaemia 227\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEirini G. Velliou, Maria Fuentes‐Gari, Ruth Misener, Eleni Pefani, Nicki Panoskaltsis, Athanasios\u003c\/i\u003e \u003ci\u003eMantalaris, Michael C. Georgiadis, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Towards a Personalised Treatment for Leukaemia: From in vivo to in vitro and in silico 227\u003c\/p\u003e \u003cp\u003e6.2 In vitro Block of the Integrated Platform for the Study of Leukaemia 228\u003c\/p\u003e \u003cp\u003e6.3 In silico Block of the Integrated Platform for the Study of Leukaemia 229\u003c\/p\u003e \u003cp\u003e6.4 Bridging the Gap Between in vitro and in silico 231\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 In vitro Studies: Acute Myeloid Leukaemia 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEirini G. Velliou, Eleni Pefani, Susana Brito dos Santos, Maria Fuentes‐Gari, Ruth Misener, Nicki\u003c\/i\u003e \u003ci\u003ePanoskaltsis, Athanasios Mantalaris, Michael C. Georgiadis, and Efstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Description of Biomedical System 233\u003c\/p\u003e \u003cp\u003e7.1.1 The Human Haematopoietic System 233\u003c\/p\u003e \u003cp\u003e7.1.2 General Structure of the Bone Marrow Microenvironment 235\u003c\/p\u003e \u003cp\u003e7.1.3 The Cell Cycle 236\u003c\/p\u003e \u003cp\u003e7.1.4 Leukaemia: The Disease 238\u003c\/p\u003e \u003cp\u003e7.1.5 Current Medical Treatment 239\u003c\/p\u003e \u003cp\u003e7.2 Experimental Part 240\u003c\/p\u003e \u003cp\u003e7.2.1 Experimental Platforms 240\u003c\/p\u003e \u003cp\u003e7.2.2 Crucial Environmental Factors in an in vitro System 241\u003c\/p\u003e \u003cp\u003e7.2.2.1 Environmental Stress Factors and Haematopoiesis 241\u003c\/p\u003e \u003cp\u003e7.2.3 Growth and Metabolism of an AML Model System as Influenced by Oxidative and Starvation Stress: A Comparison Between 2D and 3D Cultures 244\u003c\/p\u003e \u003cp\u003e7.2.3.1 Materials and Methods 244\u003c\/p\u003e \u003cp\u003e7.2.3.2 Results and Discussion 247\u003c\/p\u003e \u003cp\u003e7.2.3.3 Conclusions 254\u003c\/p\u003e \u003cp\u003e7.3 Cellular Biomarkers for Monitoring Leukaemia in vitro 255\u003c\/p\u003e \u003cp\u003e7.3.1 (Macro‐)autophagy: The Cellular Response to Metabolic Stress and Hypoxia 255\u003c\/p\u003e \u003cp\u003e7.3.2 Biomarker Candidates 256\u003c\/p\u003e \u003cp\u003e7.3.2.1 (Autophagic) Biomarker Candidates 256\u003c\/p\u003e \u003cp\u003e7.3.2.2 (Non‐autophagic) Stress Biomarker Candidates 257\u003c\/p\u003e \u003cp\u003e7.4 From in vitro to in silico 257\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 In silico Acute Myeloid Leukaemia 265\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEleni Pefani, Eirini G. Velliou, Nicki Panoskaltsis, Athanasios Mantalaris, Michael C. Georgiadis, and\u003c\/i\u003e \u003ci\u003eEfstratios N. Pistikopoulos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 265\u003c\/p\u003e \u003cp\u003e8.1.1 Mathematical Modelling of the Cell Cycle 266\u003c\/p\u003e \u003cp\u003e8.1.2 Pharmacokinetic and Pharmacodynamic Mathematical Models in Cancer Chemotherapy 268\u003c\/p\u003e \u003cp\u003e8.1.2.1 PK Mathematical Models 269\u003c\/p\u003e \u003cp\u003e8.1.2.2 PD Mathematical Models 273\u003c\/p\u003e \u003cp\u003e8.2 Chemotherapy Treatment as a Process Systems Application 273\u003c\/p\u003e \u003cp\u003e8.2.1 Physiologically Based Patient Model for the Treatment of AML With DNR and Ara‐C 275\u003c\/p\u003e \u003cp\u003e8.2.2 Design of an Optimal Treatment Protocol for Chemotherapy Treatment 277\u003c\/p\u003e \u003cp\u003e8.2.3 Mathematical Model Analysis Using Patient Data 278\u003c\/p\u003e \u003cp\u003e8.2.3.1 Model Sensitivity Analysis 278\u003c\/p\u003e \u003cp\u003e8.2.3.2 Patient Data 279\u003c\/p\u003e \u003cp\u003e8.2.3.3 Estimation of Patient‐specific Cell Cycle Parameters 280\u003c\/p\u003e \u003cp\u003e8.3 Analysis of a Patient Case Study 282\u003c\/p\u003e \u003cp\u003e8.3.1 First Chemotherapy Cycle 282\u003c\/p\u003e \u003cp\u003e8.3.2 Second Chemotherapy Cycle 282\u003c\/p\u003e \u003cp\u003e8.4 Conclusions 285\u003c\/p\u003e \u003cp\u003eAppendix 8A Mathematical Model 286\u003c\/p\u003e \u003cp\u003eAppendix 8B Patient Data 290\u003c\/p\u003e \u003cp\u003eReferences 296\u003c\/p\u003e \u003cp\u003eIndex 301\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406957683031,"sku":"9781118965597","price":999.99,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/modelling-optimization-and-control-of-biomedical-systems-9781118965597","provider":"Book Curl","version":"1.0","type":"link"}