Systems Immunology

This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes.

1 Introduction to basic concepts in immunology Roxana Khazen and Salvatore Valitutti 2 Overview of mechanistic modeling: Techniques, approximations, and assumptions Steven M. Abel 3 The fundamentals of statistical data analysis William C. L. Stewart 4 Using data to guide model construction: Application of principal component analysis and related methods in immunology research Kathryn Miller-Jensen 5 An introduction to rule-based modeling of immune receptor signaling John A.P. Sekar and James R. Faeder 6 Boolean models in immunology Reinhard Laubenbacher and Elena Dimitrova 7 From evolutionary computation to phenotypic spandrels: Inverse problem for immune ligand recognition Paul Francois and Mathieu Hemery 8 Zen and the art of parameter estimation in systems biology Christopher R. Myers 9 Spatial kinetics in immunological modeling Daniel Coombs and Byron Goldstein 10 Analysis and modeling of single cell data Derya Altintan, Jascha Diemer, and Heinz Koeppl 11 Quantifying lymphocyte receptor diversity Thierry Mora and Aleksandra M. Walczak 12 Antigen receptor diversification during immune responses Miri Michaeli and Ramit Mehr 13 Quantitative modeling of mast cell signaling Lily A. Chylek, David A. Holowka, Barbara A. Baird, and William S. Hlavacek 14 Physical models in immune signaling Jayajit Das 15 Modeling and inference of cell population dynamics Michael Flossdorf and Thomas Hoefer 16 Population dynamics of host and pathogens Amber M. Smith, Ruy M. Ribeiro, and Alan S. Perelson 17 Viral fitness landscapes: A physical sciences perspective Gregory R. Hart and Andrew L. Ferguson 18 A wish-list for modeling immunological synapses Michael L. Dustin