Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model
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2025-06-01
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PLoS Computational Biology
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Public Library of Science (PLoS)
Melbourne Authors
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School of Mathematics and Statistics
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Browning, A. P., Crossley, R. M., Villa, C., Maini, P. K., Jenner, A. L., Cassidy, T. & Hamis, S. (2025). Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. Plos Computational Biology, 21 (6), pp.e1013202-. https://doi.org/10.1371/journal.pcbi.1013202.
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Abstract
Phenotypic plasticity contributes significantly to treatment failure in many cancers. Despite the increased prevalence of experimental studies that interrogate this phenomenon, there remains a lack of applicable quantitative tools to characterise data, and importantly to distinguish between resistance as a discrete phenotype and a continuous distribution of phenotypes. To address this, we develop a stochastic individual-based model of plastic phenotype adaptation through a continuously-structured phenotype space in low-cell-count proliferation assays. That our model corresponds probabilistically to common partial differential equation models of resistance allows us to formulate a likelihood that captures the intrinsic noise ubiquitous to such experiments. We apply our framework to assess the identifiability of key model parameters in several population-level data collection regimes; in particular, parameters relating to the adaptation velocity and cell-to-cell heterogeneity. Significantly, we find that cell-to-cell heterogeneity is practically non-identifiable from both cell count and proliferation marker data, implying that population-level behaviours may be well characterised by homogeneous ordinary differential equation models. Additionally, we demonstrate that population-level data are insufficient to distinguish resistance as a discrete phenotype from a continuous distribution of phenotypes. Our results inform the design of both future experiments and future quantitative analyses that probe phenotypic plasticity in cancer.