Scaling of joint mass and metabolism fluctuations in in silico cell-laden spheroids
| Autor: | Amos Maritan, Chiara Magliaro, Ermes Botte, Francesco Biagini, Arti Ahluwalia, Andrea Rinaldo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
body-size
Quantitative Biology::Tissues and Organs Pipeline (computing) In silico finite element models Cell Culture Techniques Probability density function spheroids Models Biological form Joint probability distribution Spheroids Cellular evolution Range (statistics) Covariations Finite element models Scaling Spheroids Allometric scaling laws Probability Allometric scaling metabolic rate Kleiber's law Physics Kleiber's law metabolic rate Multidisciplinary scaling Computational Biology Biological Sciences Models Theoretical general-model covariations Oxygen Biophysics and Computational Biology Metabolism Multidimensional Scaling Analysis oxygen-consumption Allometry Biological system |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| Popis: | Significance Allometric scaling has many applications, from the prediction of pharmacokinetics in animals and humans to the probing of ecosystem dynamics. Most studies have neglected to account for variations and fluctuations, although they are intrinsic features of all biological systems. To understand how metabolic scaling emerges in the presence of variations, we developed computer-generated models of cell-laden spheroids to define the experimental size range of cell cultures with quantifiable similitudes in terms of fluctuations and metabolic scaling with living organisms. We show that the estimates of scaling exponents may change with increasing variability in both mass and metabolic rate. The computational pipeline described underpins the sound design of statistically meaningful cell-based models, with impacts in both biomedical science and ecology. 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 metabolic-rate 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 of more predictive and physiologically relevant in vitro models. |
| Databáze: | OpenAIRE |
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