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Exploring Host-Pathogen Relationships Through Computer Simulations of Intracellular Infection Garrett Am Rc Dancik Iowa State University

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Exploring Host-Pathogen Relationships Through Computer Simulations of Intracellular Infection Garrett Am Rc Dancik Iowa State University Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 2008 Exploring host-pathogen relationships through computer simulations of intracellular infection Garrett aM rc Dancik Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Bioinformatics Commons Recommended Citation Dancik, Garrett aM rc, "Exploring host-pathogen relationships through computer simulations of intracellular infection" (2008). Retrospective Theses and Dissertations. 15834. https://lib.dr.iastate.edu/rtd/15834 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Exploring host-pathogen relationships through computer simulations of intracellular infection by Garrett Marc Dancik A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Bioinformatics and Computational Biology Program of Study Committee: Karin Dorman, Co-major Professor Douglas Jones, Co-major Professor Alicia Carriquiry David Fern´andez-Baca Drena Dobbs Iowa State University Ames, Iowa 2008 Copyright c Garrett Marc Dancik, 2008. All rights reserved. 3316191 Copyright 2008 by Dancik, Garrett Marc All rights reserved 3316191 2008 ii DEDICATION For Potamus iii TABLE OF CONTENTS LISTOFTABLES ................................... vii LISTOFFIGURES .................................. viii ACKNOWLEDGEMENTS.............................. x ABSTRACT....................................... xi CHAPTER1. Generalintroduction . 1 1.1 Thesisorganization.............................. .... 1 1.2 The immune response to intracellular pathogen . .......... 2 1.2.1 Innateimmunity ............................... 2 1.2.2 Antigen processing and presentation . ...... 3 1.2.3 The adaptive immune response . 3 1.3 Leishmania infection ................................. 5 1.3.1 Clinicaldiseases .............................. 5 1.3.2 Transmission ................................. 6 1.3.3 Immunology of Leishmania major ..................... 7 1.3.4 Treatmentandvaccination . 8 1.4 Systems biology and computer modeling . ....... 9 1.4.1 The systems biology approach . ... 9 1.4.2 Cellular automata and agent-based models . ..... 10 iv 1.4.3 Simulationenvironments. 12 1.5 Gaussian processes and computer model prediction . ........... 14 1.5.1 Overview ................................... 14 1.5.2 The Gaussian process as a computer model approximation........ 15 1.5.3 ExperimentalDesign. 19 1.6 Gaussian process prediction and simulation-based based research . 21 CHAPTER 2. Parameter estimation and sensitivity analysis in an agent- based model of Leishmania major infection.................. 32 2.1 Introduction.................................... 33 2.2 An agent-based model of Leishmania major infection. 36 2.2.1 Themodelenvironment . 37 2.2.2 Stagesofinfection ............................. 37 2.3 Statisticalmethods.............................. 42 2.3.1 The Gaussian process as a computer model surrogate . ....... 43 2.3.2 Gaussian process diagnostics . 46 2.3.3 Sensivity analysis using the Gaussian process . ......... 46 2.3.4 Computer model calibration using field observations . .......... 48 2.3.5 MarkovChainMonteCarlo . 50 2.3.6 Bayesianmodelcomparison . 51 2.4 Results......................................... 53 2.4.1 Experimental design and computer model output . ....... 53 2.4.2 Fitting the Gaussian processes . 54 2.4.3 Sensitivityanalysis. 55 2.4.4 Calibration using simulated data . ..... 57 2.4.5 Calibrationusingfielddata . 58 v 2.4.6 Comparison of alternative models of macrophage behavior........ 59 2.5 Discussion...................................... 60 2.5.1 Gaussian process computer model approximation . ........ 61 2.5.2 Sensitivityanalysis. 62 2.5.3 Calibration using simulated data . ..... 65 2.5.4 Calibrationusingfielddata . 66 2.5.5 Model comparison and macrophage emigration . ...... 70 2.6 Conclusionsandfuturework . 72 2.7 Appendix: Agent-based model parameters . ....... 73 CHAPTER 3. Identifying correlates of optimal immune escape strategies and immune escape success in an in silico model of viral infection . 93 3.1 Introduction.................................... 94 3.2 MaterialsandMethods. 96 3.2.1 Acomputermodelofviralinfection . 96 3.2.2 Computermodelanalysis . 99 3.3 Results......................................... 102 3.3.1 Effect of computer model parameters on simulation output . 102 3.3.2 Correlates of successful immune escape . .......104 3.3.3 Simultaneous cellular and humoral escape . .......107 3.3.4 Viral fitness and immune escape success . 108 3.4 Discussion...................................... 109 CHAPTER 4. mlegp: statistical analysis for computer models of biological systemsusingR .................................. 129 4.1 Introduction.................................... 130 vi 4.2 Statisticalmethods.............................. 131 4.2.1 TheGaussianprocess ............................ 131 4.2.2 Sensitivityanalysis. 132 4.3 Software........................................ 133 4.4 Example application: analysis of a computer model of disease ..........133 4.5 SupplementaryMaterials . 136 4.5.1 Methods....................................136 4.5.2 ResultsandDiscussion. 136 CHAPTER 5. General conclusions and future work . 142 5.1 Host-pathogen interactions and emergent behavior in intracellular infections . 142 5.2 Futurework...................................... 143 vii LIST OF TABLES 2.6 ABM parameters and values used in initial calibration . ....... 74 2.1 Gaussianprocessdiagnostics...................... 78 2.2 FANOVAdecomposition .......................... 78 2.3 Posterior summary of computer model parameters following calibra- tionusingsimulatedfielddata. 78 2.4 Posterior summary of computer model parameters following calibra- tionusingfielddata............................. 82 2.5 Posterior summary of computer model parameters following calibra- tionusingfielddata............................. 82 2.7 Updated ranges for ABM parameters following initial calibration . 87 3.1 Subset of computer model parameters . 115 3.2 Proportion of escape mutants that fail to establish secondary infection ...................................116 3.3 Consistency of optimal escape strategies for each pair of inj- ectiontimes .................................118 3.4 Effect of viral fitness on immune escape success . 119 4.1 FANOVA Decomposition of Gaussian process predictors for pathogen loadatfiveand18dpi ...........................139 viii LIST OF FIGURES 2.1 Simulated output over time and the field data used in calibration . 75 2.2 Main effects of computer model parameters on simulated pathogen loadovertime................................ 76 2.3 Gaussianprocessdiagnostics...................... 77 2.4 Main effects of the most influential parameters . ..... 79 2.5 Two-wayinteractionplots ......................... 80 2.6 Posterior distributions of αI , kI , βMrecr, and Tthreshold following cali- brationusingsimulatedfielddata . 81 2.7 Posterior distributions of αI , kI , and Tthreshold following calibration usingfielddata ............................... 83 2.8 Posterior predictive distribution of macrophage counts and parasite load 84 2.9 Posterior distribution of Anecr following calibration using field data . 85 2.10 Main effects of computer model parameters on simulated macrophage countsovertime............................... 86 3.1 Subset of C-IMMSIM rules and parameters . 114 3.2 Simulation output clustered according to plasma viral load . 116 3.3 Classification tree for simulated plasma virus load profiles .......117 ix 3.4 Regression tree for overall relative humoral importance against com- putermodelparameters . .118 3.5 Correlation of various immune response characteristics with peak plasma humoral and cellular escape mutants over time . 119 3.6 Correlation of various immune response characteristics with dpeak = peak plasma viral load (CEM) - peak plasma viral load (HEM) . 120 3.7 Classification tree for optimal immune escape strategy using overall relative importance of the humoral response . 121 3.8 Peak plasma viral load for cellular, humoral and dual escape mutants when injected at tpeak ............................122 3.9 Cellular and humoral escape mutants have synergistic effects. .123 3.10 Humoral escape mutants that are less infectious are more successful than escape mutants with the same infectivity as wild type virus. 124 4.1 Sensitivity analysis of a computer model of parasitic infection using the R package mlegp ............................132 4.2 Gaussian process diagnostic plots . 138 4.3 Main effects of all parameters using the Guassian process predictors for pathogen load at five and 18 dpi. 138 4.4 Interaction effects of the most important interactions for pathogen load atfiveand18dpi...............................140 4.5 Gaussian process (GP) diagnostic plots for pathogen for a GP with constant nugget term and GP with user-specified diagonal nugget matrix ....................................141 x ACKNOWLEDGEMENTS I would like to take this opportunity to express my thanks to those who helped me with various aspects of conducting research and the writing of this thesis. I am particularly grateful for the advice and encouragement
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