Computational Immunology

<p>1. Introduction to Computational Immunology </p> <p>Overview</p> <ol> </ol> <p>Modeling tools and techniques</p> <ol> </ol> <p>Use Cases Illustrating the Application of Computational Immunology Technologies</p> <ol> </ol> <p>2. Computational Modeling </p> <ol> </ol> <p>Overview on Computational Modeling</p> <ol> </ol> <p>Translational Research Iterative Modeling Cycle</p> <ol> </ol> <ul> <ul> </ul> <li>Information and knowledge extraction from the Literature</li> <li>Collect new data and data from public repositories</li> <li>Model Development</li> <li>In silico Experimentation</li> <li>Validation of Computational Hypotheses and New Knowledge</li> <li>Considerations on Computational Modeling Technologies</li> <li>Computational Modeling Tools for Immunology and Infectious Disease Research</li></ul> <p>Concluding Remarks</p> <ol> </ol> <p>3. Use of Computational Modeling in Immunological Research </p> <ol> </ol> <p>Introduction</p> <ol> </ol> <p>Computational and mathematical modeling of the immune response to Helicobacter pylori</p> <ol> </ol> <ul> <ul> </ul> <li>Inflammatory bowel disease</li> <li>ODE model of CD4+ T cell differentiation</li> <li>T follicular helper cell differentiation</li> <ul> </ul></ul> <ol> </ol> <p>Concluding remarks</p> <ol> </ol> <p>4. Immunoinformatics cybernfrastructure for modeling and analytics</p> <ol> </ol> <p>Introduction</p> <ol> </ol> <p>Web Portal</p> <ol> </ol> <p>LabKey-based Laboratory Information Management System</p> <ol> </ol> <p>Public Repositories: ImmPort</p> <ol> </ol> <p>Global gene expression analysis</p> <ol> </ol> <p>High Performance Computing Environment</p> <ol> </ol> <p>HPC infrastructure for ENISI MSM modeling</p> <ol> </ol> <p>CyberInfrastructure for NETwork science (CINET)</p> <ol> </ol> <p>Pathosystems Resource Integration Center (Patric)</p> <ol> </ol> <p>Clinical Data Integration</p> <ol> </ol> <p>Concluding Remarks</p> <ol> </ol> <p>5. Ordinary Differential Equations (ODE) based Modeling </p> <ol> </ol> <p>Introduction</p> <ol> </ol> <p>ODE based modeling pipeline</p> <ol> </ol> <ul> <ul> </ul> <li>Model development</li> <li>Model Calibration</li> <li>Deterministic simulations</li> <li>Sensitivity analysis</li> <li>Model driven hypothesis generation</li> <ul> </ul></ul> <ol> </ol> <p>Case studies: CD4+ T cell differentiation model</p> <ol> </ol> <p>Concluding Remarks</p> <ol> </ol> <p>6. Agent-Based Modeling and High Performance Computing</p> <ol> </ol> <p>Introduction and basic definitions</p> <ol> </ol> <p>Related work</p> <ol> </ol> <p>Technical implementation of ENISI</p> <ol> </ol> <p>Formal Representation of ENISI</p> <ol> </ol> <p>Agent Based Modeling using ENISI</p> <ol> </ol> <p>Calibration and validation of the preliminary model</p> <ol> </ol> <p>Sensitivity Analysis for ABM</p> <ol> </ol> <p>Scaling the sensitivity analysis calculations</p> <ol> </ol> <p>Scalability and Performance</p> <ol> </ol> <p>Modeling Study investigating immune responses to H. pylori</p> <ol> </ol> <ul> <ul> </ul> <li>Use case: Predictive computational modeling of the mucosal immune responses during H. pylori infection</li> <ul> </ul></ul> <ol> </ol> <p>Concluding remarks</p> <ol> </ol> <p>7. From Big Data Analytics and Network Inference to Systems Modeling </p> <ol> </ol> <p>Introduction</p> <ol> </ol> <p>Big Bata drives Big Models</p> <ol> </ol> <ul> <ul> </ul> <li>Experimental planning and power analysis</li> <li>RNA-Seq analysis pipeline</li> <li>Read summarization</li> <li>Differential expression analysis</li> <li>Time series data</li> <li>Unsupervised high-resolution clustering</li> <ul> </ul></ul> <ol> </ol> <p>Tools, techniques and pipelines</p> <ul> <ul> </ul> <li>RNA-Seq analysis in the cloud</li> <li>RNA Rocket at the PAThosystems Resource Integration Center</li> <li>Network inference and analytics</li> <li>Supervised Machine learning methods</li> <li>NetGenerator</li> <li>Adaptive Robust Integrative Analysis for finding Novel Association (ARIANA)</li> <li>Case study: Reconstructing the Th17 differentiation networkConcluding remarks</li> </ul> <p>8. Multiscale Modeling: Concepts, Technologies, and Use Cases in Immunology </p> <ol> </ol> <p>Introduction</p> <ol> </ol> <p>Multiscale modeling concepts and techniques</p> <ol> </ol> <ul> <ul> </ul> <li>Modeling Technologies and Tools</li> <li>From Single Scale to Multiscale Modeling</li> <ul> </ul></ul> <ol> </ol> <p>Sensitivity analysis</p> <ol> </ol> <ul> <ul> </ul> <li>Global versus local sensitivity analysis</li> <li>Sparse experimental design for sensitivity analysis</li> <li>Temporal significance of modeling parameters</li> <li>Sensitivity analysis across scales</li> <ul> </ul></ul> <ol> </ol> <p>Multiscale Modeling of Mucosal Immune Responses</p> <ol> </ol> <ul> <ul> </ul> <li>The scales of ENISI platform</li> <li>Challenges and opportunities</li> <ul> </ul></ul> <ol> </ol> <p>Case Study</p> <ol> </ol> <ul> <ul> </ul> <li>Modeling mucosal immunity in the Gut</li> <li>Multiscale modeling of mucosal immune responses</li> <ul> </ul></ul> <ol> </ol> <p>Concluding remarks</p> <ol> </ol> <p>9. Modeling exercises</p> <ol> </ol> <p>Modeling tools</p> <ol> </ol> <p>Models</p> <ol> </ol> <ul> <ul> </ul> <li>Computational model of immune responses to Clostridium difficile infection</li> <li>Computational model of the 3-node T helper type 17 model</li> <li>Computational model of the 9-node Th1/Th17/Treg model</li> <ul> </ul></ul> <ol> </ol> <p>Model complexity and model-driven hypothesis generation</p> <ol> </ol> <p>Concluding remarks</p>