Computational Immunology

Computational Immunology : Models and Tools

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Description

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

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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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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