biostudies-literatureRajakaruna HNIAID NIH HHSNIHNIGMS NIH HHS103https://www.ebi.ac.uk/biostudies/studies/S-EPMC9548402biostudies-literatureUnknown84(10)Mathematical modeling provides a rigorous way to quantify immunological processes and discriminate between alternative mechanisms driving specific biological phenomena. It is typical that mathematical models of immunological phenomena are developed by modelers to explain specific sets of experimental data after the data have been collected by experimental collaborators. Whether the available data are sufficient to accurately estimate model parameters or to discriminate between alternative models is not typically investigated. While previously collected data may be sufficient to guide development of alternative models and help estimating model parameters, such data often do not allow to discriminate between alternative models. As a case study, we develop a series of power analyses to determBulletin of mathematical biologyMathematical Modeling to Guide Experimental Design: T Cell Clustering as a Case Study.PMC9548402R01AI158963R01 GM118553R01 AI158963Ganusov VVRajakaruna HfalseMathematical Modeling to Guide Experimental Design: T Cell Clustering as a Case Study.Mathematical modeling provides a rigorous way to quantify immunological processes and discriminate between alternative mechanisms driving specific biological phenomena. It is typical that mathematical models of immunological phenomena are developed by modelers to explain specific sets of experimental data after the data have been collected by experimental collaborators. Whether the available data are sufficient to accurately estimate model parameters or to discriminate between alternative models is not typically investigated. While previously collected data may be sufficient to guide development of alternative models and help estimating model parameters, such data often do not allow to discriminate between alternative models. As a case study, we develop a series of power analyses to determ2022-01-01T00:00:00Z2022 Aug2025-04-20T03:01:49.166Z2025-04-20T03:01:49.166ZS-EPMC95484023597804710.1007/s11538-022-01063-x