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Decoding immune kinetics: unveiling secrets using custom-built mathematical models

Nature Methods volume 21, pages 744–747 (2024)Cite this article

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Custom-built mathematical models make the immune response more predictable and offer mechanistic insights into fundamental immunology.

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Fig. 1: Overview of mechanistic modeling in immunology.

References

  1. Myers, M. A. et al. eLife 10, e68864 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jenner, A. L. et al. PLoS Pathog. 17, e1009753 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Butner, J. D. et al. Sci. Adv. 6, eaay6298 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mitchell, S. Front. Cell Dev. Biol. 8, 616592 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  5. De Boer, R. J. & Yates, A. J. Annu. Rev. Immunol. 41, 513–532 (2023).

    Article  PubMed  Google Scholar 

  6. Burt, P. & Thurley, K. Sci. Adv. 9, eadg7668 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Alber, M. et al. NPJ Digit. Med. 2, 115 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Smith, A. M. Immunol. Rev. 285, 97–112 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Laubenbacher, R. et al. NPJ Syst. Biol. Appl. 10, 19 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wessler, T. et al. PLOS Comput. Biol. 16, e1007280 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sen, S., Cheng, Z., Sheu, K. M., Chen, Y. H. & Hoffmann, A. Cell Syst. 10, 169–182.e5 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Handel, A., La Gruta, N. L. & Thomas, P. G. Nat. Rev. Immunol. 20, 186–195 (2020).

    Article  CAS  PubMed  Google Scholar 

Download references

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.

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  1. Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA

    Amber M. Smith

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  1. Amber M. Smith
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Correspondence to Amber M. Smith.

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Nature Methods thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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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

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