Computational Immunology

Computational Immunology : Models and Tools

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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.
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Product details

  • Paperback | 210 pages
  • 191 x 235 x 15.24mm | 390g
  • Academic Press Inc
  • San Diego, United States
  • English
  • black & white illustrations
  • 0128036974
  • 9780128036976

Table of contents

1. Introduction to Computational Immunology


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


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


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


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


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
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


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


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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About Josep Bassaganya-Riera

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.
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