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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-12-803697-6 (9780128036976)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Autor*in
Professor of Immunology & Director, Nutritional Immunology & Molecular Medicine Laboratory (NIMML) and Center for Modeling Immunity to Enteric Pathogens (MIEP), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA
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
