Scaling of joint mass and metabolism fluctuations in in silico cell-laden spheroids

• Published in: Proceedings of the National Academy of Sciences - PNAS 2021-09, Vol.118 (38), p.1-9
• Main Authors: Botte, Ermes, Biagini, Francesco, Magliaro, Chiara, Rinaldo, Andrea, Maritan, Amos, Ahluwalia, Arti
• Format: Article
• Language: English
• Subjects: Biological Sciences; Cell Culture Techniques - methods; Cell size; Computational Biology - methods; Computer applications; Diffusion rate; Estimation; Exponents; Fluctuations; Metabolic rate; Metabolism; Metabolism - physiology; Models, Biological; Models, Theoretical; Multidimensional Scaling Analysis; Oxygen; Oxygen - metabolism; Pipelines; Probability; Probability density functions; Scaling; Spheroids; Spheroids, Cellular - metabolism; Statistical analysis
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2025211118

Variations and fluctuations are characteristic features of biological systems and are also manifested in cell cultures. Here, we describe a computational pipeline for identifying the range of three-dimensional (3D) cell-aggregate sizes in which nonisometric scaling emerges in the presence of joint mass and metabolic rate fluctuations. The 3D cell-laden spheroids with size and single-cell metabolic rates described by probability density functions were randomly generated in silico. The distributions of the resulting metabolic rates of the spheroids were computed by modeling oxygen diffusion and reaction. Then, a method for estimating scaling exponents of correlated variables through statistically significant data collapse of joint probability distributions was developed. The method was used to identify a physiologically relevant range of spheroid sizes, where both nonisometric scaling and a minimum oxygen concentration (0.04 mol·m−3) is maintained. The in silico pipeline described enables the prediction of the number of experiments needed for an acceptable collapse and, thus, a consistent estimate of scaling parameters. Using the pipeline, we also show that scaling exponents may be significantly different in the presence of joint mass and metabolicrate variations typically found in cells. Our study highlights the importance of incorporating fluctuations and variability in size and metabolic rates when estimating scaling exponents. It also suggests the need for taking into account their covariations for better understanding and interpreting experimental observations both in vitro and in vivo and brings insights for the design ofmore predictive and physiologically relevant in vitro models.