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In silico model development and optimization of in vitro lung cell population growth.
Mostofinejad, Amirmahdi; Romero, David A; Brinson, Dana; Marin-Araujo, Alba E; Bazylak, Aimy; Waddell, Thomas K; Haykal, Siba; Karoubi, Golnaz; Amon, Cristina H.
Afiliación
  • Mostofinejad A; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Romero DA; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Brinson D; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Marin-Araujo AE; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Bazylak A; Latner Research Laboratories, Division of Thoracic Surgery, University Health Network, Toronto, Ontario, Canada.
  • Waddell TK; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Haykal S; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Karoubi G; Latner Research Laboratories, Division of Thoracic Surgery, University Health Network, Toronto, Ontario, Canada.
  • Amon CH; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
PLoS One ; 19(5): e0300902, 2024.
Article en En | MEDLINE | ID: mdl-38748626
ABSTRACT
Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Proliferación Celular / Pulmón / Modelos Biológicos Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Proliferación Celular / Pulmón / Modelos Biológicos Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos