Transcription-translation machinery -- an autocatalytic network coupling all cellular cycles and generating a plethora of growth laws

TL;DR

This paper presents a unifying model based on an autocatalytic transcription-translation network that explains bacterial growth laws, linking cellular components and growth rates through fundamental cycles and deriving new quantitative relations.

Contribution

It introduces a universal autocatalytic network model for bacterial growth, deriving multiple growth laws and explaining how various cellular processes influence growth rates.

Findings

Derived growth laws relating growth rate to catalyst fractions and cycle time scales.

Explained the impact of gene expression and antibiotics on growth rate.

Predicted effects of temperature and cellular perturbations on growth dynamics.

Abstract

Recently discovered simple quantitative relations, known as bacterial growth laws, hint on the existence of simple underlying principles at the heart of bacterial growth. In this work, we provide a unifying picture on how these known relations, as well as new relations that we derive, stems from a universal autocatalytic network common to all bacteria, facilitating balanced exponential growth of individual cells. We show that the core of the cellular autocatalytic network is the transcription -- translation machinery -- in itself an autocatalytic network comprising several coupled autocatalytic cycles, including the ribosome, RNA polymerase, and tRNA charging cycles. We derive two types of growth laws per autocatalytic cycle, one relating growth rate to the relative fraction of the catalyst and its catalysis rate, and the other relating growth rate to all the time scales in the cycle. The structure of the autocatalytic network generates numerous regimes in state space, determined by the limiting components, while the number of growth laws can be much smaller. We also derive a growth law that accounts for the RNA polymerase autocatalytic cycle, which we use to explain how growth rate depends on the inducible expression of the rpoB and rpoC genes, which code for the RpoB and C protein subunits of RNA polymerase, and how the concentration of rifampicin, which targets RNA polymerase, affects growth rate without changing the RNA-to-protein ratio. We derive growth laws for tRNA synthesis and charging, and predict how growth rate depends on temperature, perturbation to ribosome assembly, and membrane synthesis.