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| موضوع: كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R الثلاثاء 06 أكتوبر 2020, 11:03 am | |
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أخوانى فى الله أحضرت لكم كتاب Modeling and Control of Infectious Diseases in the Host With Matlab and R Esteban A. Hernandez-Vargas Frankfurt am Main, Germany Series Editor Edgar Sánchez
و المحتوى كما يلي :
Contents About the Author . ix Preface . xi Acknowledgments . xiii Part 1 Theoretical Biology Principles Chapter 1 Introduction .3 1.1 Modeling and Control of Infectious Diseases 3 1.2 Basics of Immunology . 5 1.3 Basics of Virology . 13 1.4 Viral Mutation and Drug Resistance 15 CHAPTER 2 Mathematical Modeling Principles .19 2.1 Mathematical Modeling . 19 2.2 Mathematical Preliminaries . 21 2.3 Dynamical Systems . 27 2.4 Population Modeling . 29 CHAPTER 3 Model Parameter Estimation 35 3.1 Parameter Fitting . 35 3.2 Experimental Data . 38 3.3 Cost Function . 40 3.4 Optimization Problem 42 3.5 Identifiability . 46 3.6 Bootstrapping Parameters 50 3.7 Sources of Errors and Limitations . 51 3.8 Concluding Remarks 59 PART 2 MODELING HOST INFECTIOUS DISEASES CHAPTER 4 Modeling Influenza Virus Infection .65 4.1 Influenza Infection . 65 4.2 Modeling Influenza Infection . 67 4.3 Influenza Infection Model With Immune Response . 73 4.4 Postinfluenza Susceptibility to Pneumococcal Coinfection . 76 4.5 Concluding Remarks 81 CHAPTER 5 Modeling Ebola Virus Infection .85 5.1 Ebola Infection . 85 5.2 In Vitro Ebola Virus Infection Model 87 5.3 In Vivo Ebola Virus Infection and Vaccination 94 5.4 Concluding Remarks 101 viiviii Contents CHAPTER 6 Modeling HIV Infection 105 6.1 HIV Infection . 105 6.2 HIV Disease Progression . 107 6.3 How Does HIV Cause AIDS? 108 6.4 Mathematical Modeling of HIV Infection . 110 6.5 The Three Stages in HIV Infection 111 6.6 Concluding Remarks 125 CHAPTER 7 HIV Evolution During Treatment .129 7.1 Antiretroviral Drugs for HIV Infection 129 7.2 Guidelines for HAART Treatment . 131 7.3 Including HAART in Mathematical Models 134 7.4 Basic Viral Mutation Treatment Models 136 7.5 Model With Reservoirs That Replicate Virus Frequently 142 7.6 Mutation Model With Latent CD4+ T Reservoirs . 146 7.7 Concluding Remarks 150 PART 3 ADVANCED TOPICS IN CONTROL THEORY CHAPTER 8 Optimal Therapy Scheduling .155 8.1 Optimal Control Background . 155 8.2 Positive Switched Linear Systems . 159 8.3 Optimal Control for Positive Switched Systems 160 8.4 Optimal Control to Mitigate HIV Escape 163 8.5 Restatement as an Optimization Problem 177 8.6 Dynamic Programming for Positive Switched Systems . 179 8.7 Concluding Remarks 191 CHAPTER 9 Suboptimal Therapy Scheduling .193 9.1 Control of Switched Systems . 193 9.2 Continuous-Time Guaranteed Cost Control . 194 9.3 Discrete-Time Guaranteed Cost Control 196 9.4 Model Predictive Control . 202 9.5 Mitigating HIV Escape Simulations 205 9.6 Concluding Remarks 209 CHAPTER 10 PK/PD-based Impulsive Control .211 10.1 Introduction . 211 10.2 Inverse Optimal Impulsive Control 212 10.3 Tailoring Influenza Treatment 216 10.4 Concluding Remarks 219 Bibliography . 221 Index . 237 Index A Abortive cells, 109 Abortive infection, 109, 111 Acute infections, xi, 32, 37, 107, 216 Adaptive cell immune response, 95 Adaptive immune response, 5, 6, 8, 9, 32, 94 Adaptive immune response dynamics, 71 Adaptive immune system, 5 Administrated drugs, 217 Adrenal cortical cells, 86 Adult thymus, 109 AIDS, 105, 107–109, 111, 112, 115, 117, 118, 120, 126, 127, 129, 132 in HIV infection, 127 worldwide, 105 Alternating therapy group, 146, 150 Antibody antigen specificity, 12 response, 13, 95, 98, 100, 102, 103 Anticancer drug, 129 Antigen, 5, 7, 9, 10, 97, 98, 102 EBOV, 97 MHC class, 10 peptide fragments, 7 receptor, 12 Antigenic material stimulation, 13 Antiretroviral drugs, 129, 131, 134 Antiretroviral drugs for HIV infection, 129 Antiretroviral therapy, 3, 129, 131, 135, 163 Antiviral drugs, 216 therapy, 4 therapy combination, 134 Autonomous switched systems, 161 B Bacterial coinfection, 77, 82 infection, 3, 77, 78 outgrowth during coinfections, 81 Block infection, 135 Blood cells, 131 C Cells CTLs, 113 immune, 8, 10, 83, 98 immune system, 9 in HIV infection, 19 in humans, 113 in lymph nodes, 109 Characteristic across infections, 21 Chronic infections, 32, 33 Chronic viral infection, 32 Clinical observations, 122, 123, 142, 144, 146 progression, 133 trial SWATCH, 208 Coating antigens, 12 Coinfection, 76–78 Control Lyapunov function (CLF), 214 Controlling EBOV infection, 95 Cost function, 36, 39–42, 44, 45, 54, 155, 156, 177, 180, 192, 196, 198, 200, 201 minimization, 42 weighting, 176, 206 Cost functional, 161, 162 optimal, 162, 164, 180 CTLs, 67, 70, 71, 73, 83 cells, 113 homeostatic, 73 proliferation, 73 replenishment, 73 Cytotoxic cells, 10 D Daughter cells, 10 Dendritic cells (DCs), 6, 7, 86, 97, 107, 109, 113 Differential evolution (DE), 42, 58 Diluted virus suspension, 78 Discontinuous therapy, 133 Disease during coinfection, 80 Disease progression, 107, 109, 111, 127 HIV, 107, 111 DNA viruses, 15 Drug, 133–136, 139, 140, 144, 149, 211, 216, 218 administration, 211 combination, 136, 138, 143, 144 combination mathematical model, 137, 148 concentration, 216 development, 211 dynamics, 216 effects, 144 efficiency, 135, 149, 216, 218 intake, 217 phases, 216 projects, 211 resistance, 133, 136 resistance mutations, 134 therapies, 137, 138, 144, 149, 207, 208 treatments, 139 237238 Index Dynamics, xi, 29, 30, 54, 59, 60, 70–73, 95, 97, 115, 119, 126, 137, 142, 146, 148, 150, 217, 219 antibody, 95, 102 drug, 216 HIV, 137 influenza infection, 67, 70 influenza virus, 37, 67 macrophages, 119, 135 viral, 31, 38, 67, 100, 120, 137, 163 virus, 37, 152, 217 Dynamics modeling, 211 E Ebola infection, 85 viral load, 91 virus, 85, 86, 101 virus infection, 86, 87 virus infection vaccination, 94 EBOV, 85–88, 94, 99, 101 antigen, 97 boosted IgG dynamics, 98 challenge, 96 disease, 85 glycoprotein, 95, 96 infection, 86, 87, 92, 94, 95, 97–99, 101–103 kinetics, 86 natural, 85 prevention, 102 replication, 95–97, 99, 103 replication dynamics, 96 target cells, 87 titers, 96, 102 vaccination, 97, 103 EBOV infection, 95 Eclipse phase, 66, 68, 69, 72 Egg infection, 72 Emerging virus diseases, 3 Endothelial cells, 86 Epithelial cells, 65, 70, 86, 101 Equilibrium point, 27–29, 79, 80, 212 Equine influenza infection, 70 Equine influenza virus, 72 Extracellular antigens, 11 F Facilitating infection, 109 Filoviridae virus infection, 87 Filovirus virions, 85 Fitness, 17, 72, 139, 143, 144, 148, 149 Fitness genotype, 144, 149 Fitness viral, 72 Flu infection, 31 Foreign antigens, 7 Fusion inhibitors (FI), 129 G Genotype, 15, 17, 137, 138, 143, 144, 148, 149 fitness, 144, 149 highly resistant, 150 Guaranteed cost, 193, 194, 206–209 Guaranteed cost control, 194, 196, 207 H HAART, 129, 131–133, 135, 136, 150 experience, 142 in mathematical models, 134 regimens, 132, 134 treatment, 131, 135, 146, 150 Highly resistant genotype (HRG), 138 HIV, 108 disease progression, 107, 111 drug resistance, 132 dynamics, 137 infection, xi, 15, 17, 105, 110–113, 115–118, 127, 132, 133, 135, 136, 147, 150, 204, 208, 211 in humans, 125 mathematical modeling, 110 progression, 136 primary infection, 107 reservoirs, 147 RNA, 131–135 RNA levels, 150 therapy, 131 Humans, 3, 85, 95, 101, 216 cells in, 113 immune system, 86 immunodeficiency virus, 105 infected, 14 influenza, 69 I IgG dynamics, 98 IgG dynamics in EBOV infection, 97 IgG titers, 96, 98, 101 Immune cells, 8, 10, 83, 98 response dynamics, 70 responses, 4, 5, 9, 19, 67, 69–71, 73, 85, 95, 101–103 responses dynamics, 98 system, xi, 5–7, 10, 32, 66, 67, 71, 74, 82, 94, 101, 102, 105, 107, 109, 110, 117, 131, 133, 140 cells, 9 components, 82 responses, 15, 65, 110, 114, 124 Impaired immune response, 70 Infected cells, 5, 7, 31, 32, 38, 53, 67, 69–71, 73, 87, 88, 92–94, 113, 114, 125, 135, 148, 208 Infected cells eclipse phase, 72Index 239 Infected humans, 105 Infected macrophages, 10, 112, 113, 115, 119, 124–126, 135, 143, 146 Infection, 7, 14, 15, 31, 32, 53, 55, 65, 68, 70, 72, 78, 79, 85–87, 93–95, 97, 99, 101, 105, 109–111, 114, 115, 117, 118, 120, 124, 125, 127, 131, 133, 150 bacterial, 3, 77, 78 course, 95 cycle, 15 Ebola, 85 EBOV, 86, 87, 92, 94, 95, 97–99, 101–103 HIV, xi, 15, 17, 105, 110–113, 115–118, 127, 132, 133, 135, 136, 147, 150, 204, 208, 211 influenza, 56, 65–67, 69–71, 73, 80–82, 216 influenza virus, 39, 52, 92 kinetics, 53, 91 process, 113 rate, 88, 135, 142, 148 rates for macrophages, 116 viral, 3, 4, 7, 10, 19, 32, 36, 54, 70, 71, 73, 86–88, 91, 219 virus, 38 Infectious diseases, xi, 3, 4, 21, 211 Infectious virus, 15, 111 Infectious virus particles, 114 Influenza, xi, 32, 38, 44, 53, 65–67, 69–71, 73, 74, 76–78, 83, 84 acute symptoms, 65 genetic factors, 82 humans, 69 infection, 56, 65–67, 69–71, 73, 80–82, 216 dynamics, 67, 70 model parameters, 217 infections dissecting, 67 mathematical modeling, 67 pandemic, 76 prophylaxis, 83 specific antibodies, 67 strains, 72 vaccination, 82 vaccines, 67 virus, 72, 88, 91, 94 dynamics, 37, 67 infection, 39, 52, 92 infection dynamics, 82 strains, 83 Influenza A virus (IAV), 53, 65, 78 Initiating antiretroviral therapy, 132 Initiating therapy, 131 Innate immune responses, 6, 71, 95, 102, 103 Innate immune system, 5, 6, 10, 67 K Kidney epithelial cells, 87 L Langerhans cells, 124 Latent infection, 112 Latent reservoirs, 146 Latently infected cells, 110, 148, 150, 208 Least square (LS) estimation, 58 Lethal EBOV challenge, 94 viral load, 100 Linear decreasing factors, 144, 149, 150 Linear programming (LP), 182 Linear quadratic (LQ), 156 Lymph nodes, 6, 7, 10, 97, 109, 111, 125 Lymphocyte cell count, 132, 135 M mAbs dynamics, 95 Macrophages, 6, 8, 9, 86, 97, 106, 107, 110, 112–115, 118, 120, 121, 124–127, 135, 142, 144, 146, 152 dynamics, 119, 135 dynamics in HIV infection, 121 in HIV infection, 112 infection rate, 113, 122, 126 population, 126 Major histocompatibility complex (MHC), 8 Mathematical model, xi, 19–21, 29, 31, 36–38, 46, 51, 52, 60, 61, 67, 69–73, 81, 83, 84, 86, 102, 110–112, 114, 120, 122, 134, 136–138, 150 model in influenza infections, 67 model in viral infection, 53 model parameters, 59, 60 modeling, 4, 19, 20, 35, 52, 53, 55, 59, 65, 72, 77, 81, 111, 125, 211 modeling approach, 19, 36, 83 Maximum likelihood estimation (MLE), 41, 58 MDCK cells infection, 72240 Index Minimum cost function, 157 Mitigate viral escape, 165 Model parameters, xi, 20, 35, 36, 38, 44, 49, 52, 53, 57–60, 73, 74, 78 influenza infection, 217 mathematical, 59, 60 Model predictive control (MPC), 193 Mononuclear phagocytic cells, 8 Monte Carlo (MC) simulations, 218 Mouse infection, 77 Multiplicities of infection (MOI), 87 Mutation rate, 15, 137, 138, 143, 148, 166, 178 N Natural killer (NK) cells, 6, 10, 67 Nerve cells, 7 Neuraminidase inhibitors, 69, 216 NK cells, 10, 70, 71, 83 Nucleotide reverse transcriptase inhibitors (NRTI), 129, 131 O Opportunistic infections, 105, 108 Optimal control, 155–157, 160–164, 166, 171, 174, 177, 182, 185, 186, 191, 193, 202, 203, 205, 206, 208, 209, 212–214 law, 206, 213–215 policies, 176, 212 problem, 155, 156, 161, 164, 166, 171, 177, 191, 193, 209 cost functional, 162, 164, 180 switching signal, 162, 166, 178, 180 Ordinary differential equations (ODE), 27, 31 P Parameter accuracy, 38, 57, 58, 60 estimation, 4, 20, 35–37, 54, 55, 58, 60, 61, 69 algorithms, 58 problems, 35, 75 procedures, 36, 52, 53 set, 35, 40, 41, 48 Pathogen, 5, 8, 9, 12, 32, 33 Pathogen living, 8 Peptide antigens, 7 Persistent reservoirs, 110 Peyers patches (PP), 10 Phagocytic cells, 97 Pharmacodynamics, 211 Plasma cells, 11 Plasma virus level, 135 Pluripotent hematopoietic stem cells, 6 Pneumococcal coinfection, 76 Pneumoniae infection, 78, 81 Pontryagin solution, 164, 165, 167, 168, 170, 171, 173, 176 Population dynamics, 21, 69, 114 Population modeling, 21, 29 Postinfluenza susceptibility, 76 Potent antiretroviral drugs, 129 Proactive switching, 134, 151, 165, 177, 185, 186, 192, 207 Productively infected cells, 68, 109 Progression clinical, 133 HIV infection, 136 of HIV, 120 to AIDS, 111, 112, 117, 118, 126, 127, 129, 133 Protease inhibitors (PI), 129, 131 Protection against infections, 12 Protective immune response, 66 Provirus, 106 R Recombinant vesicular stomatitis virus, 96 Recycling therapies, 146 Reemerging viruses, 3 Replication rate, 88, 92, 115, 124, 137–139, 144, 149, 166, 191 Reproductive number, 33, 93 Reproductive number in EBOV infection, 93 Reservoirs, 110, 112, 126, 127, 131, 136, 142, 146, 150 dynamics, 126 HIV, 147 viral, 112 Resistant genotypes, 141 Respiratory tract infection, 130 Retrovirus, 106, 125 Reverse transcriptase inhibitors (RTI), 135 Ring vaccination trial, 94 RNA viruses, 15, 65, 106 RNA viruses mutation rates, 17Index 241 Root mean square error (RMSE), 41, 54 Root mean square (RMS), 41 S Secondary infection dynamics, 71 Simulation results, 30, 54, 58, 60, 111, 112, 119, 124, 125, 136, 142, 146, 150, 152, 206, 208, 209 Specialized cells, 6 Stable equilibrium point, 27 Stable infection, 133 Structured treatment interruptions (STI), 133 Susceptible cells, 32, 67, 87, 94 Susceptible cells infection rate, 32, 87, 92 Susceptible population, 33, 94 SWATCH, 146, 151, 207 approach, 142, 146, 150 strategy, 141 therapy, 150 treatment, 141 Switched systems, 159–162, 179, 180, 191, 193, 194 T T cell receptors (TCR), 9 Target cells, xi, 31, 32, 38, 44, 53, 54, 60, 67, 69–73, 76, 86 EBOV, 87 infection, 72 infection rate, 71 model parameters, 75 Therapy, 4, 19, 132–135, 138–143, 146, 148–150, 176, 177, 186, 191, 207, 208 alternation, 176, 192 combination, 166 for virologic failure, 140 HIV, 131 in HIV infection, 165 sequencing, 134 SWATCH, 150 withdrawal, 133 Therapy failure, 140 Thymic epithelial cells, 109 Thymus, 6, 9, 10, 109–111 Tumor necrosis factor (TNF), 70 U UNAIDS estimates, 105 Uncomplicated influenza infection dynamics, 69 Uninfected cells, 9, 53, 67 Uninfected macrophages, 112, 119 V Vaccination, 83, 84, 94, 98, 100–102 EBOV, 97, 103 efficacy, 83 influenza, 82 strategies, 65, 84 Vero cells, 87 Viral clearance, 44, 53, 54, 66, 69, 75, 88, 89, 99, 138, 166, 176 clearance rate, 76, 137 dynamics, 31, 38, 67, 100, 120, 137, 163 escape, 163, 176, 177, 194, 206, 207, 209 explosion, 118, 124, 126, 207 fitness, 72 genotypes, 137 infection, 3, 4, 7, 10, 19, 32, 36, 54, 70, 71, 73, 86–88, 91, 219 infection dynamics, 69 kinetics, 71, 72, 87, 91 load dynamic, 121 mutation, 15, 136, 140, 167, 169, 179 mutation rates, 15, 138, 176 replication, 13, 15, 44, 53, 59, 70, 88, 94, 97, 99, 100, 105, 106, 111, 113, 115, 124, 129, 131, 134, 135, 139, 146, 148, 150 replication dynamic, 98 reservoirs, 112 strains, 4, 15, 137, 169 titers, 15, 38–40, 67, 70–72, 74, 86, 87, 92, 96, 99–101 Virologic failure, 132–134, 140–142, 144, 146, 150, 165, 176, 207, 208 treatment, 141, 146, 150, 208 Virus, 13–15, 17, 31, 32, 44, 53, 65–67, 71, 73, 74, 78, 85–87, 92–96, 105, 107–113, 117, 120, 125, 131, 136, 146, 217 clearance, 71 dilutions, 15 dynamics, 37, 152, 217 Ebola, 85, 86, 101 eradication, 112 growth, 13, 74 healthy cells infection, 115 infection, 38 infection dynamics in humans, 101 infectivity, 66 influenza, 72, 88, 91, 94 inoculum, 53 modeling, 36 pandemic, 76242 Index particle release dynamics, 72 particles, 13, 31, 32, 87, 105, 114 pathogenic, 65 production, 72, 106 proteins, 14 quantification, 15 replication, 13, 66, 97, 113, 115, 142, 148 strain, 55, 72, 77 surface, 216 titer, 76, 92 W World Health Organization (WHO), 3
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