In silico model development and optimization of in vitro lung cell population growth

• Published in: PloS one Vol. 19; no. 5; p. e0300902
• Main Authors: Mostofinejad, Amirmahdi, Romero, David A, Brinson, Dana, Marin-Araujo, Alba E, Bazylak, Aimy, Waddell, Thomas K, Haykal, Siba, Karoubi, Golnaz, Amon, Cristina H
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.05.2024; Public Library of Science (PLoS)
• Subjects: Alveoli; Biology and Life Sciences; Bioreactors; Cell culture; Cell Line; Cell populations; Cell Proliferation; Cell research; Computer Simulation; Design of experiments; Design optimization; Differential equations; Engineering and Technology; Epithelial Cells - cytology; Epithelial Cells - metabolism; Experimental design; Glucose; Glucose - metabolism; Growth; Growth media; Humans; Lung - cytology; Lung - metabolism; Mathematical models; Methods; Models, Biological; Optimization; Oxygen - metabolism; Parameters; Partial differential equations; Physical Sciences; Population biology; Population growth; Research and Analysis Methods; Stem cells; Substrates; Tissue engineering; Tissue Engineering - methods; Canada
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0300902

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.