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