Mathematical, Computational and Experimental T Cell Immunology

Description

Mathematical, statistical, and computational methods enable multi-disciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. This volume collects a representative sample of studies in T cell immunology that illustrate the benefits of modelling-experimental collaborations and which have proven valuable or even ground-breaking. Studies include thymic selection, T cell repertoire diversity, T cell homeostasis in health and disease, T cell-mediated immune responses, T cell memory, T cell signalling and analysis of flow cytometry data sets. Contributing authors are leading scientists in the area of experimental, computational, and mathematical immunology. Each chapter includes state-of-the-art and pedagogical content, making this book accessible to readers with limited experience in T cell immunology and/or mathematical and computational modelling. Contents 1 Cytokine Receptor Signaling and CD4/CD8 Lineage Choice during T Cell Development in the Thymus 1.1 Introduction 1.2 T Cell Development and CD4/CD8 Lineage Choice in the Thymus 1.3 Cytokine Requirement in T Cell Development 1.4 Cytokine Signaling in the CD4/CD8 Lineage Choice of Developing Thymocytes 1.5 Cytokine-Driven Molecular Circuitry of CD4/CD8 Lineage Choice 1.6 IL-7 Receptor Expression during T Cell Development 1.7 Cytokine Signaling in the Post-Selection Maturation of Thymocytes 1.8 Conclusion and Perspectives References 2 An Agent-Based Model of T Helper Cell Fate Decisions in the Thymus 2.1 Introduction 2.1.1 Affinity Model of Thymic Selection 2.1.2 Selection of Regulatory T Cells 2.2 Model 2.2.1 Representation of Proteins and Their Binding Affinity 2.2.2 TCR-pMHC Interaction Surface 2.2.3 Signal Integration 2.2.3.1 Fate Decision Requires Multiple Interactions 2.2.3.2 Integrated TCR Signal 2.2.4 Simulation Settings: Thymocyte-APC Interactions and Signal Integration 2.2.4.1 T Cells and In Silico Scanning of APCs 2.2.5 Dynamics of Integrated TCR Signal and Fate Determination 2.2.5.1 SSL-Based Positive Selection 2.2.5.2 TSL-Based Negative Selection 2.2.5.3 SSL-Based nTreg Selection 2.3 Simulation Results 2.3.1 TCR Specificity to MHC and Cognate Peptide Are Encoded into Different Components of the Integrated TCR Signal 2.3.2 Distribution of Affinity to Self-Peptides in Preselection Repertoire Is Preserved in Tconv and Treg Repertoires 2.3.3 Thymocytes with Low MHC Affinity Are Neglected in Positive Selection 2.3.4 Treg Repertiore Is Enriched with MHC-Specific TCRs 2.3.5 Positive and Negative Selections Confine the Repertoire to an Intermediate Maximum spMHC Affinity 2.4 Conclusions and Biological Implications References 3 Modelling Naive T Cell Homeostasis 3.1 Introduction 3.2 The Biological Processes Underpinning T Cell Survival and Proliferative Renewal 3.3 Regulation of Naive T Cell Numbers in Mice 3.3.1 Is the Compartment Size Regulated by Competition for a Global Resource? 3.3.2 Limitations of the Explanatory Power of Resource-Competition Models 3.3.3 Characterising Heterogeneity Within the Naive T Cell Pools 3.3.4 The Cyton Framework and Its Equivalence to Other Models 3.4 Naive T Cell Homeostasis in Humans 3.4.1 The Evidence for Density-Dependent Regulation of Cell Numbers 3.4.2 Estimating the Relative Contributions of Thymic Output and Peripheral Division 3.5 TCR-Specific Niches and Repertoire Diversity 3.6 The Dynamics of Recent Thymic Emigrants in Mice and Humans References 4 Mechanistic Models of CD4 T Cell Homeostasis and Reconstitution in Health and Disease 4.1 Introduction 4.2 Models of CD4 T Cell Homeostasis 4.2.1 TRECs 4.2.2 DNA Labelling Studies 4.3 Modelling the Development of CD4 T Cell Homeostasis 4.3.1 Quantifying Changes in Thymic Activity 4.3.1.1 Relating the One- and Two-Compartment Models 4.3.2 Age-Related Changes in T Cell Division and Death Rates 4.4 Homeostasis and Reconstitution in Disease 4.4.1 HIV 4.4.1.1 Models Including Viral Dynamics 4.4.1.2 Modelling T Cell Reconstitution on Antiretroviral Therapy 4.4.1.3 T Cell Homeostasis During Chronic HIV Infection 4.4.2 Haematopoietic Stem Cell Transplantation 4.5 Conclusions References 5 Modeling the Dynamics of CD4+ T Cells in HIV-1 Infection 5.1 Introduction 5.2 Viral Dynamics to Measure Infected Cell Dynamics 5.3 Assays to Measure T-Cell Turnover 5.3.1 Ki-67 Content of T-Cells 5.3.2 Labeling of Dividing Cells 5.4 Turnover of T-Cells in HIV Infection 5.5 Conclusions References 6 Modelling the Response to Interleukin-7 Therapy in HIV-Infected Patients 6.1 IL-7 Therapy for HIV-Infected Patients 6.1.1 Biological Background 6.1.2 Clinical Perspectives 6.2 Mathematical Model of the Response to Exogenous IL-7 Therapy 6.2.1 General Model 6.2.2 A First Basic Model 6.2.3 More Complex Models 6.2.3.1 Introduction of Feedback Term 6.2.3.2 Introducing the Dose Effect 6.2.3.3 Use of a PK/PD Model 6.2.3.4 Distinguishing the Effect of Each Single Injection 6.2.3.5 Repeated Cycles of Injection 6.2.3.6 Further Improvement 6.3 Estimating the Model Parameters Using Real Data 6.3.1 Principle and Methods 6.3.2 Results 6.4 Predictions of the Model 6.4.1 Using Predictions for Designing Clinical Trials 6.4.2 Using Predictions for Personalized Medicine 6.5 Conclusion References 7 Modeling Immunopathology During Persistent Viral Infections 7.1 Introduction 7.2 Simple Model for Immune Exhaustion 7.2.1 Modeling Immunopathology 7.2.2 Dynamics of Infection and Immune Response Exhaustion 7.2.3 Preexisting CD8 T Cell Immunity and Immunopathology 7.3 Multi-epitope Model for Exhaustion 7.3.1 How Dynamics of Infection and Pathology Depend on the Breadth of the Vaccination 7.4 Model for Immune Escape 7.4.1 Immune Escape Does Not Change the Relationship Between Pathology and Pre-immunity Level 7.5 Concluding Comments References 8 Delayed Differentiation Makes Many Models Compatible with Data for CD8+ T Cell Differentiation 8.1 Introduction 8.2 Models and Results 8.2.1 Linear Models 8.2.2 Branching and Fate Models 8.2.3 Variations in Family Sizes 8.3 Conclusion 8.4 Methods References 9 Inferring Differentiation Order in Adaptive Immune Responses from Population-Level Data 9.1 Introduction 9.2 Experiments and Results from Relevant Papers 9.3 The Mathematical Model and Its Adaptation & Fit to Cohort Data Reported in Buchholz630 9.4 Adaptation and Application to Cohort Data from Badovinac2007,SchlubMain,Kinjyo2015 9.4.1 Adaptation 9.4.2 Application 9.5 Discussion References 10 Experimental and Mathematical Approaches to Quantify Recirculation Kinetics of Lymphocytes 10.1 Introduction 10.2 Mathematical Modeling of Lymphocyte Recirculation 10.2.1 Spleen 10.2.2 Lymph Nodes 10.2.3 Whole Body Recirculation Kinetics 10.2.4 Recirculation of Activated Lymphocytes in Mice 10.2.5 Recirculating and Non-recirculating Lymphocytes 10.3 Summary References 11 The Public Face and Private Lives of T Cell Receptor Repertoires 11.1 Introduction 11.2 T Cell Receptor Gene Organization, Locus, and Rearrangement 11.3 Landscape of the TCR Repertoire 11.4 Molecular Basis of Public and Private TCRs 11.4.1 Genetic/Epigenetic Bias 11.4.2 Bias in RSS Targeting 11.4.3 Ease of Generation and Convergent Recombination 11.5 Determinants of Public and Private T Cell Responses 11.5.1 Role of Recombinatorial Bias 11.5.2 Role of Thymic Selection 11.5.3 Role of Antigen-Driven Selection 11.6 Functional Associations with Public and Private Receptors 11.6.1 Protective and Beneficial Roles of Public Receptors 11.6.2 Pathogenic Associations with Public and Private TCRs 11.7 Public Receptors in Diagnosis, Prognosis, and Therapeutics 11.8 Concluding Remarks References 12 Population Dynamics of Immune Repertoires 12.1 Introduction 12.2 General Model 12.3 Neutral Evolutionary Theory 12.4 Competition for Resources in Constant Environments 12.5 Fluctuating Antigenic Environments 12.6 Effect of Competition on Large Clones in Fluctuating Environments 12.7 Non-specific Resources 12.8 Aging of Immune Systems 12.9 Conclusion References 13 Mathematical Modelling of T Cell Activation 13.1 The T Cell Response 13.2 The T Cell Signalling Network 13.3 Phenotypic Features of the T Cell Response 13.4 Mathematical Models of T Cell Activation 13.4.1 Occupancy Model 13.4.2 Kinetic Proofreading 13.4.3 Negative Regulator of KPR 13.4.4 KPR with Competing Regulators 13.4.5 KPR with Limited Signalling 13.4.6 KPR with Limited Signalling and IFF 13.5 Summary and Future Directions References 14 Agent-Based Model of Heterogeneous T-Cell Activation in Vitro 14.1 Introduction 14.2 Kinetics of T-Cell Activation In Vitro and In Silico 14.2.1 The Computational Model 14.3 Discussion 14.4 Experimental Materials and Methods A Python Code References 15 CTLA-4-Mediated Ligand Trans-Endocytosis:A Stochastic Model 15.1 Introduction 15.2 The Stochastic Model 15.3 Stochastic Descriptors 15.3.1 Steady-State CTLA-4 Probability Distribution 15.3.2 Time to Reach a Threshold Number I of Internalised Ligands 15.3.3 Maximum Number of Bound Complexes on the Cell Surface 15.4 Parameters: Kinetic Rates and Molecular Counts 15.5 Results 15.5.1 Ligand Depletion Timescales 15.5.2 Formation of Bound Complexes 15.6 Discussion A Matrix-Analytic Approach A.1 The Time to Reach a Threshold Number I of Internalised Ligands A.2 The Maximum Number of Bound Complexes Simultaneously Present on the Cell Surface References 16 Automated Gating and Dimension Reduction of High-Dimensional Cytometry Data 16.1 Introduction 16.2 A Tool Based on Finite Mixtures of Multivariate Skew Normal Factor Analyzers 16.2.1 Modelling Cell Populations 16.2.2 Dimension Reduction for High-Dimensional Data 16.3 Parameter Estimation via EM Algorithm 16.3.1 E-Step 16.3.2 M-Step 16.4 Analysis of High-Dimensional CyTOF Data 16.5 Conclusions References Index