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SpheroidSim-Preliminary evaluation of a new computational tool to predict the influence of cell cycle time and phase fraction on spheroid growth.
Little, J P; Pettet, G J; Hutmacher, D W; Loessner, D.
Afiliación
  • Little JP; Queensland University of Technology (QUT), Brisbane, QLD, Australia.
  • Pettet GJ; Queensland University of Technology (QUT), Brisbane, QLD, Australia.
  • Hutmacher DW; Queensland University of Technology (QUT), Brisbane, QLD, Australia.
  • Loessner D; Australian Research Centre for Additive Biomanufacturing, QUT, Brisbane, QLD, Australia.
Biotechnol Prog ; 34(6): 1335-1343, 2018 11.
Article en En | MEDLINE | ID: mdl-30009492
BACKGROUND: There is a relative paucity of research that integrates materials science and bioengineering with computational simulations to decipher the intricate processes promoting cancer progression. Therefore, a first-generation computational model, SpheroidSim, was developed that includes a biological data set derived from a bioengineered spheroid model to obtain a quantitative description of cell kinetics. RESULTS: SpheroidSim is a 3D agent-based model simulating the growth of multicellular cancer spheroids. Cell cycle time and phases mathematically motivated the population growth. SpheroidSim simulated the growth dynamics of multiple spheroids by individually defining a collection of specific phenotypic traits and characteristics for each cell. Experimental data derived from a hydrogel-based spheroid model were fit to the predictions providing insight into the influence of cell cycle time (CCT) and cell phase fraction (CPF) on the cell population. A comparison of the number of active cells predicted for each analysis showed that the value and method used to define CCT had a greater effect on the predicted cell population than CPF. The model predictions were similar to the experimental results for the number of cells, with the predicted total number of cells varying by 8% and 12%, respectively, compared to the experimental data. CONCLUSIONS: SpheroidSim is a first step in developing a biologically based predictive tool capable of revealing fundamental elements in cancer cell physiology. This computational model may be applied to study the effect of the microenvironment on spheroid growth and other cancer cell types that demonstrate a similar multicellular clustering behavior as the population develops. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1335-1343, 2018.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biotechnol Prog Asunto de la revista: BIOTECNOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biotechnol Prog Asunto de la revista: BIOTECNOLOGIA Año: 2018 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Estados Unidos