Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model.
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 resistan...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013202 |
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| _version_ | 1849319474392465408 |
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| author | Alexander P Browning Rebecca M Crossley Chiara Villa Philip K Maini Adrianne L Jenner Tyler Cassidy Sara Hamis |
| author_facet | Alexander P Browning Rebecca M Crossley Chiara Villa Philip K Maini Adrianne L Jenner Tyler Cassidy Sara Hamis |
| author_sort | Alexander P Browning |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bfb6b4c68ad74a8ab6cbc024ae069a57 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-bfb6b4c68ad74a8ab6cbc024ae069a572025-08-20T03:50:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-06-01216e101320210.1371/journal.pcbi.1013202Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model.Alexander P BrowningRebecca M CrossleyChiara VillaPhilip K MainiAdrianne L JennerTyler CassidySara HamisPhenotypic 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.https://doi.org/10.1371/journal.pcbi.1013202 |
| spellingShingle | Alexander P Browning Rebecca M Crossley Chiara Villa Philip K Maini Adrianne L Jenner Tyler Cassidy Sara Hamis Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. PLoS Computational Biology |
| title | Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. |
| title_full | Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. |
| title_fullStr | Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. |
| title_full_unstemmed | Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. |
| title_short | Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model. |
| title_sort | identifiability of phenotypic adaptation from low cell count experiments and a stochastic model |
| url | https://doi.org/10.1371/journal.pcbi.1013202 |
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