Systems Immunology
An Introduction to Modeling Methods for Scientists
Herausgeber: Das, Jayajit; Jayaprakash, Ciriyam
Systems Immunology
An Introduction to Modeling Methods for Scientists
Herausgeber: Das, Jayajit; Jayaprakash, Ciriyam
- Broschiertes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
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.
Andere Kunden interessierten sich auch für
- William SchiesserNumerical Modeling of COVID-19 Neurological Effects68,99 €
- Mike KirbyOn-Treatment Verification Imaging68,99 €
- Justin R YatesDeterminants of Addiction123,99 €
- Ralph SteinmanHuman Immunology180,99 €
- Managing Treatment-Resistant Depression204,99 €
- Clinical Allergy and Asthma Management in Adolescents and Young Adults118,99 €
- Nanomedicine for Inflammatory Diseases65,99 €
-
-
-
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.
Produktdetails
- Produktdetails
- Verlag: Taylor and Francis
- Seitenzahl: 334
- Erscheinungstermin: 31. März 2021
- Englisch
- Abmessung: 254mm x 178mm x 19mm
- Gewicht: 617g
- ISBN-13: 9780367780920
- ISBN-10: 0367780925
- Artikelnr.: 61211512
- Verlag: Taylor and Francis
- Seitenzahl: 334
- Erscheinungstermin: 31. März 2021
- Englisch
- Abmessung: 254mm x 178mm x 19mm
- Gewicht: 617g
- ISBN-13: 9780367780920
- ISBN-10: 0367780925
- Artikelnr.: 61211512
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 2. Overview of Mechanistic
Modeling: Techniques, Approximations, and Assumptions 3. The Fundamentals
of Statistical Data Analysis 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 6. Boolean Models in Immunology 7. From evolutionary
computation to phenotypic spandrels: inverse problem for immune ligand
recognition 8. Zen and the Art of Parameter Estimation in Systems Biology
9. Spatial Kinetics in Immunological Modeling 10. Analysis and modeling of
single cell data 11. Quantifying lymphocyte receptor diversity 12. Antigen
receptor diversification during immune responses 13. Quantitative Modeling
of Mast Cell Signaling 14. Physical models in immune signaling 15.
Population Dynamics of Host and Pathogens 16. Viral fitness landscapes: A
physical sciences perspective 17. A wish-list for modeling immunological
synapses
Modeling: Techniques, Approximations, and Assumptions 3. The Fundamentals
of Statistical Data Analysis 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 6. Boolean Models in Immunology 7. From evolutionary
computation to phenotypic spandrels: inverse problem for immune ligand
recognition 8. Zen and the Art of Parameter Estimation in Systems Biology
9. Spatial Kinetics in Immunological Modeling 10. Analysis and modeling of
single cell data 11. Quantifying lymphocyte receptor diversity 12. Antigen
receptor diversification during immune responses 13. Quantitative Modeling
of Mast Cell Signaling 14. Physical models in immune signaling 15.
Population Dynamics of Host and Pathogens 16. Viral fitness landscapes: A
physical sciences perspective 17. A wish-list for modeling immunological
synapses
1. Introduction to Basic Concepts in Immunology 2. Overview of Mechanistic
Modeling: Techniques, Approximations, and Assumptions 3. The Fundamentals
of Statistical Data Analysis 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 6. Boolean Models in Immunology 7. From evolutionary
computation to phenotypic spandrels: inverse problem for immune ligand
recognition 8. Zen and the Art of Parameter Estimation in Systems Biology
9. Spatial Kinetics in Immunological Modeling 10. Analysis and modeling of
single cell data 11. Quantifying lymphocyte receptor diversity 12. Antigen
receptor diversification during immune responses 13. Quantitative Modeling
of Mast Cell Signaling 14. Physical models in immune signaling 15.
Population Dynamics of Host and Pathogens 16. Viral fitness landscapes: A
physical sciences perspective 17. A wish-list for modeling immunological
synapses
Modeling: Techniques, Approximations, and Assumptions 3. The Fundamentals
of Statistical Data Analysis 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 6. Boolean Models in Immunology 7. From evolutionary
computation to phenotypic spandrels: inverse problem for immune ligand
recognition 8. Zen and the Art of Parameter Estimation in Systems Biology
9. Spatial Kinetics in Immunological Modeling 10. Analysis and modeling of
single cell data 11. Quantifying lymphocyte receptor diversity 12. Antigen
receptor diversification during immune responses 13. Quantitative Modeling
of Mast Cell Signaling 14. Physical models in immune signaling 15.
Population Dynamics of Host and Pathogens 16. Viral fitness landscapes: A
physical sciences perspective 17. A wish-list for modeling immunological
synapses