"Taken together, the body of information contained in this book provides readers with a bird's-eye view of different aspects of exciting work at the convergence of disciplines that will ultimately lead to a future where we understand how immunity is regulated, and how we can harness this knowledge toward practical ends that reduce human suffering. I commend the editors for putting this volume together."
-Arup K. Chakraborty, Robert T. Haslam Professor of Chemical Engineering, and Professor of Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
New experimental techniques in immunology have produced large and complex data sets that require quantitative modeling for analysis. 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. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined.
Readers from immunology, quantitative biology, and applied physics will benefit from the following:
- Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels.
- An overview of basic concepts in modeling and data analysis.
- Coverage of topics where mechanistic modeling has contributed substantially to current understanding.
- Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.
Jayajit Das, Ph.D., is Assistant Professor of Pediatrics at the Wexner Medical Center, The Ohio State University, and Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, both in Columbus, Ohio. He obtained his Ph.D. in statistical physics from The Institute of Mathematical Sciences and Raman Research Institute, India. He was a postdoctoral research associate at Virginia Tech, University of California, Berkeley, and Massachusetts Institute of Technology, prior to joining OSU. He is a member of the American Physical Society, author of many published journal articles, and invited speaker at numerous international conferences.
Ciriyam Jayaprakash, Ph.D., is Professor in the Department of Physics at The Ohio State University in Columbus, Ohio. He earned his doctorate in physics from the University of Illinois at Urbana-Champaign, and was a postdoctoral associate at Cornell University as well as a visiting scientist at the IBM Watson Research Center prior to joining the faculty of OSU. He is the recipient of a prestigious Alfred P. Sloan Foundation Fellowship and NSF Presidential Young Investigator Award, and is an elected Fellow of the American Physical Society. His current research interests include modeling of viral antagonists and immune system response, stochastic effects in subcellular processes, and applications of nonlinear dynamics.
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
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
Paul François 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
15 Modeling and inference of cell population dynamics
Michael Flossdorf and Thomas Höfer
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