Quantifying the entropic cost of cellular growth control
Abstract
Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximumentropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genomescale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximumentropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence.
 Publication:

Physical Review E
 Pub Date:
 July 2017
 DOI:
 10.1103/PhysRevE.96.010401
 arXiv:
 arXiv:1703.00219
 Bibcode:
 2017PhRvE..96a0401D
 Keywords:

 Quantitative Biology  Molecular Networks;
 Condensed Matter  Disordered Systems and Neural Networks;
 Physics  Biological Physics;
 Quantitative Biology  Populations and Evolution;
 Quantitative Biology  Quantitative Methods
 EPrint:
 3 pages