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Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype.
Wang, Yue; Zhou, Joseph X; Pedrini, Edoardo; Rubin, Irit; Khalil, May; Taramelli, Roberto; Qian, Hong; Huang, Sui.
Afiliação
  • Wang Y; Department of Computational Medicine, University of California, Los Angeles, California, United States of America.
  • Zhou JX; Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
  • Pedrini E; Institute for Systems Biology, Seattle, Washington, United States of America.
  • Rubin I; Institute for Systems Biology, Seattle, Washington, United States of America.
  • Khalil M; Institute for Systems Biology, Seattle, Washington, United States of America.
  • Taramelli R; Institute for Systems Biology, Seattle, Washington, United States of America.
  • Qian H; Department of Theoretical and Applied Science, University of Insubria, Italy.
  • Huang S; Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
ArXiv ; 2023 Oct 19.
Article em En | MEDLINE | ID: mdl-37904742
ABSTRACT
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the uniform exponential growth model for the initial growth ("take-off"). Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article