Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
PLoS One. 2024 May 15;19(5):e0300902. doi: 10.1371/journal.pone.0300902. eCollection 2024.
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.
组织工程主要依赖于体外和体内实验来开发方案和生物反应器以生成功能性组织。作为替代方法，计算方法有可能大大缩短组织工程实验计划的时间和成本。在本文中，我们提出了一种方法来制定、选择、校准和测试数学模型，以预测细胞群体的生长作为生化环境的函数，并设计最佳的实验方案，以推断计算模型参数的模型。我们系统地结合了实验设计、数理统计和优化文献中的方法，为细胞群体动力学开发独特且可解释的数学模型。所提出的方法应用于开发第一个与气道相关的支气管肺泡上皮 (BEAS-2B) 细胞系群体的模型，该模型作为代谢相关生化底物浓度的函数。所得到的模型是一个常微分方程组，该模型预测了 BEAS-2B 细胞群体的时间动态，作为初始接种细胞群体以及生长培养基中的葡萄糖、氧气和乳酸浓度的函数，使用从最佳设计的体外实验中严格推断出的七个参数。
