Computational Immunology: Models And Tools by Josep Bassaganya-rieraComputational Immunology: Models And Tools by Josep Bassaganya-riera

Computational Immunology: Models And Tools

byJosep Bassaganya-rieraEditorJosep Bassaganya-riera

Paperback | October 27, 2015

Pricing and Purchase Info

$128.67 online 
$132.95 list price
Earn 643 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


Computational Immunology: Models and Toolsencompasses 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
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.
Title:Computational Immunology: Models And ToolsFormat:PaperbackDimensions:210 pages, 8.75 × 6.35 × 0.68 inPublished:October 27, 2015Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128036974

ISBN - 13:9780128036976

Look for similar items by category:


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 duringH. pyloriinfection

                                                                                          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
                                                                                                        • 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


                                                                                                          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 toClostridium difficileinfection
                                                                                                                                                  • 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