Custom-built mathematical models make the immune response more predictable and offer mechanistic insights into fundamental immunology.
References
Myers, M. A. et al. eLife 10, e68864 (2021).
Jenner, A. L. et al. PLoS Pathog. 17, e1009753 (2021).
Butner, J. D. et al. Sci. Adv. 6, eaay6298 (2020).
Mitchell, S. Front. Cell Dev. Biol. 8, 616592 (2021).
De Boer, R. J. & Yates, A. J. Annu. Rev. Immunol. 41, 513–532 (2023).
Burt, P. & Thurley, K. Sci. Adv. 9, eadg7668 (2023).
Alber, M. et al. NPJ Digit. Med. 2, 115 (2019).
Smith, A. M. Immunol. Rev. 285, 97–112 (2018).
Laubenbacher, R. et al. NPJ Syst. Biol. Appl. 10, 19 (2024).
Wessler, T. et al. PLOS Comput. Biol. 16, e1007280 (2020).
Sen, S., Cheng, Z., Sheu, K. M., Chen, Y. H. & Hoffmann, A. Cell Syst. 10, 169–182.e5 (2020).
Handel, A., La Gruta, N. L. & Thomas, P. G. Nat. Rev. Immunol. 20, 186–195 (2020).
Acknowledgements
A sincere thank you to all the mathematical immunologists who contribute to our field. This work was supported by NIH grants AI139088 and AI170115.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Peer review
Peer review information
Nature Methods thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
Rights and permissions
About this article
Cite this article
Smith, A.M. Decoding immune kinetics: unveiling secrets using custom-built mathematical models. Nat Methods 21, 744–747 (2024). https://doi.org/10.1038/s41592-024-02265-y
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41592-024-02265-y
- Springer Nature America, Inc.
This article is cited by
-
Investigating immunity
Nature Methods (2024)