Molina-París, C. and Lythe, G. orcid.org/0000-0001-7966-5571 (2021) Mathematical, Computational and Experimental T Cell Immunology. Springer Nature ISBN 9783030572037
Abstract
Mathematical, statistical, and computational methods enable multi-disciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. This volume collects a representative sample of studies in T cell immunology that illustrate the benefits of modelling-experimental collaborations and which have proven valuable or even ground-breaking. Studies include thymic selection, T cell repertoire diversity, T cell homeostasis in health and disease, T cell-mediated immune responses, T cell memory, T cell signalling and analysis of flow cytometry data sets. Contributing authors are leading scientists in the area of experimental, computational, and mathematical immunology. Each chapter includes state-of-the-art and pedagogical content, making this book accessible to readers with limited experience in T cell immunology and/or mathematical and computational modelling.
Metadata
Item Type: | Book |
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Authors/Creators: |
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Editors: |
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Keywords: | cell biology, data-driven modeling, thymic selection, thymus, modeling, immune |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2025 11:26 |
Last Modified: | 07 Jan 2025 11:26 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-3-030-57204-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221431 |