RT Journal Article
SR Electronic
T1 Modeling the growth of organisms validates a general relation between metabolic costs and natural selection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 358440
DO 10.1101/358440
A1 Efe Ilker
A1 Michael Hinczewski
YR 2019
UL http://biorxiv.org/content/early/2019/05/10/358440.abstract
AB Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. This hypothesis has found a recent resurgence as a powerful tool for quantitatively understanding the strength of selection among different classes of organisms. Our work explores the validity of the hypothesis from first principles through a generalized metabolic growth model, versions of which have been successful in describing organismal growth from single cells to higher animals. We build a mathematical framework to calculate how perturbations in maintenance and synthesis costs translate into contributions to the selection coefficient, a measure of relative fitness. This allows us to show that the hypothesis is an approximation to the actual baseline selection coefficient. Moreover we can directly derive the correct prefactor in its functional form, as well as analytical bounds on the accuracy of the hypothesis for any given realization of the model. We illustrate our general framework using a special case of the growth model, which we show provides a quantitative description of overall metabolic synthesis and maintenance expenditures in data collected from a wide array of unicellular organisms (both prokaryotes and eukaryotes). In all these cases we demonstrate that the hypothesis is an excellent approximation, allowing estimates of baseline selection coefficients to within 15% of their actual values. Even in a broader biological parameter range, covering growth data from multicellular organisms, the hypothesis continues to work well, always within an order of magnitude of the correct result. Our work thus justifies its use as a versatile tool, setting the stage for its wider deployment.