Computational Immunology: Models and Tools

by Josep Bassaganya-Riera

Overview

Computational Immunology: Models and Tools encompasses the methodological framework and application of cutting-edge tools and techniques to study immunological processes at a systems level, along with the concept of multi-scale modeling. The book's emphasis is on selected cases studies and application of the most updated technologies in computational modeling, discussing topics such as computational modeling and its usage in immunological research, bioinformatics infrastructure, ODE based modeling, agent based modeling, and high performance computing, data analytics, and multiscale modeling. There are also modeling exercises using recent tools and models which lead the readers to a thorough comprehension and applicability. The book is a valuable resource for immunologists, computational biologists, bioinformaticians, biotechnologists, and computer scientists, as well as all those who wish to broaden their knowledge in systems modeling. - Offers case studies with different levels of complexity - Provides a detailed view on cutting-edge tools for modeling that are useful to experimentalists with limited computational skills - Explores the usage of simulation for hypothesis generation, helping the reader to understand the most valuable points on experimental setting

Product Details

ISBN-13:	9780128037157
Publisher:	Elsevier Science & Technology Books
Publication date:	10/21/2015
Sold by:	Barnes & Noble
Format:	eBook
Pages:	210
File size:	6 MB

About the Author

Josep Bassaganya-Riera received a DVM from the College of Veterinary Medicine, Autonomous University of Barcelona, Spain in 1997 and a PhD in Immunology from Iowa State University, Ames, Iowa in 2000. He completed his Postdoc work in Nutritional Immunology at Iowa State University in 2002.

Table of Contents

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

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From the Publisher

This book provides an applied approach to generating and refining hypotheses using computational modeling and simulation studies, and is an ideal resource for immunologists, computational biologists, bioinformaticians, biotechnologists, and computer scientists, as well as all those who wish to broaden their knowledge in systems modeling.