Computational immunology : models and tools / edited by Josep Bassaganya-Riera ; contributors Vida Abedi [and eighteen others].

Format:

Book

Contributor:

Bassaganya-Riera, Josep, editor.

Abedi, Vida, contributor.

Language:

English

Subjects (All):

Immunoinformatics.

Physical Description:

1 online resource (212 p.)

Edition:

1st ed.

Place of Publication:

Amsterdam, [Netherlands] : Academic Press, 2016.

Language Note:

English

Summary:

" 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 complexityProvides a detailed view on cutting-edge tools for modeling that are useful to experimentalists with limited computational skillsExplores the usage of simulation for hypothesis generation, helping the reader to understand the most valuable points on experimental setting

Contents:

Front Cover

Computational Immunology: Models and Tools

Copyright Page

Contents

List of Contributors

1 Introduction to Computational Immunology

Overview

Modeling Tools and Techniques

Use Cases Illustrating the Application of Computational Immunology Technologies

Acknowledgments

References

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

4 Immunoinformatics Cyberinfrastructure 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

Pathosystems Resource Integration Center

Clinical Data Integration

Appendix: MIEP Data Uploaded to ImmPort

5 Ordinary Differential Equations (ODEs) Based Modeling

Modeling Network of Gene Regulation

Modeling Signaling Pathways.

Modeling Biochemical Reaction Networks

Modeling Multiple Scales

ODE-Based Modeling Pipeline

Model Calibration

Deterministic Simulations

Sensitivity Analysis

Model-Driven Hypothesis Generation

Case Studies: CD4+ T-Cell Differentiation Model

6 Agent-Based Modeling and High Performance Computing

Introduction and Basic Definitions

Related Work

Technical Implementation of ENISI

Formal Representation of ENISI

Interaction-Based Approach for Modeling Gut Mucosa: Coevolving Graphical Discrete Dynamical Systems (CGDDS)

Modeling Immune System using CGDDS

Agent-Based Modeling Using ENISI

ENISI HPC Implementation

ABM of H. pylori

Calibration and Validation of the Preliminary Model

Sensitivity Analysis for ABM

Influence of Parameters

Ranking of Parameters

Quantifying Uncertainty

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

Acknowledgment

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

Supervised Multistage Clustering to Cluster Genes Based on Pattern of Expression

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

Case Study: Reconstructing the Th17 Differentiation Network

8 Multiscale Modeling: Concepts, Technologies, and Use Cases in Immunology

MSM Concepts and Techniques

Modeling Technologies and Tools

From Single Scale to MSM

Global versus Local SA

Sparse Experimental Design for SA

Temporal Significance of Modeling Parameters

SA Across Scales

MSM of Mucosal Immune Responses

The Scales of ENISI Platform

Challenges and Opportunities

OO Design

Performance Matching

HPC-Driven MSM of Mucosal Immune Responses

Future of MSM

Case Study

Modeling Mucosal Immunity in the Gut

Multiscale Model for IBD

The Intracellular Scale

The Intercellular Scale

The Cellular Scale

Model Settings

Simulation

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

Back Cover.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters.

Description based on online resource; title from PDF title page (ebrary, viewed November 18, 2015).

ISBN:

9780128037157

0128037156

OCLC:

926101289