Computational Immunology

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