Alachkar, N., Opoku, N.K.-D.O., Monk, N.A.M. orcid.org/0000-0002-5465-4857 et al. (1 more author) (2026) Decoding cellular population dynamics through mechanistic modelling and statistical data analysis. npj Systems Biology and Applications, 12 (1). 73. ISSN: 2056-7189
Abstract
Cell-cell communication underlies key processes in development, immunity, and disease, yet capturing its mechanistic complexity remains challenging. While advances in single-cell omics have revealed new insights into cell-type diversity, mathematical modelling has become essential for deriving mechanistic understanding of their communication networks. Here, we overview established modelling approaches and highlight the need for frameworks that move beyond steady-state assumptions and single-step processes, better reflecting the nature of cell–cell communication.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Models, Biological; Humans; Cell Communication; Animals |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences |
| Date Deposited: | 14 Jul 2026 07:40 |
| Last Modified: | 14 Jul 2026 07:40 |
| Status: | Published |
| Publisher: | Springer Science and Business Media LLC |
| Refereed: | Yes |
| Identification Number: | 10.1038/s41540-026-00751-x |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:243083 |
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