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|a UAMI
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|a Computational immunology :
|b models and tools /
|c Josep Bassaganya-Riera.
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| 264 |
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1 |
|a London, UK :
|b Academic Press is an imprint of Elsevier,
|c [2015]
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|c ©2016
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|a Online resource; title from PDF title page (ScienceDirect, viewed November 4, 2015).
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|a Includes bibliographical references at the end of each chapters.
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|a 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 -- Acknowledgments -- References -- 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 -- Acknowledgments -- References -- 4 Immunoinformatics Cyberinfrastructure 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 -- Pathosystems Resource Integration Center -- Clinical Data Integration -- Concluding Remarks -- Acknowledgments -- Appendix: MIEP Data Uploaded to ImmPort -- References -- 5 Ordinary Differential Equations (ODEs) Based Modeling -- Introduction -- Modeling Network of Gene Regulation -- Modeling Signaling Pathways.
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| 505 |
8 |
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|a Modeling Biochemical Reaction Networks -- Modeling Multiple Scales -- 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 -- Acknowledgments -- References -- 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 -- Concluding Remarks -- Acknowledgment -- References -- 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 -- 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.
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| 505 |
8 |
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|a Adaptive Robust Integrative Analysis for Finding Novel Association -- Case Study: Reconstructing the Th17 Differentiation Network -- Concluding Remarks -- Acknowledgments -- References -- 8 Multiscale Modeling: Concepts, Technologies, and Use Cases in Immunology -- Introduction -- MSM Concepts and Techniques -- Modeling Technologies and Tools -- From Single Scale to MSM -- Sensitivity Analysis -- 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 -- MSM of Mucosal Immune Responses -- Concluding Remarks -- Acknowledgment -- References -- 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 -- Acknowledgment -- References -- Back Cover.
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|a Bassaganya-Riera, Josep.
|t Computational Immunology: Models and Tools.
|d : Elsevier Science, ©2015
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