# Quantifying the entropic cost of cellular growth control

## TL;DR

This paper uses a Maximum Entropy framework to quantify the regulatory effort needed for cellular growth control in E. coli and human cells, linking growth dynamics to phenotypic organization and initial colony size.

## Contribution

It introduces a MaxEnt-based method to measure the entropic cost of growth regulation and connects it to growth dynamics and initial conditions.

## Key findings

- Regulatory strategies differ between fostering and repressing growth.
- Results align with experimental data for E. coli and human cells.
- Initial colony size influences phenotypic organization during exponential growth.

## Abstract

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale models. Results obtained for E. coli and human cells are consistent with experiments and point to different regulatory strategies by which growth can be fostered or repressed. Moreover we explicitly connect the `inverse temperature' that controls MaxEnt distributions to the growth dynamics, showing that the initial size of a colony may be crucial in determining how an exponentially growing population organizes the phenotypic space.