Transcription-translation machinery -- an autocatalytic network coupling all cellular cycles and generating a plethora of growth laws
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.
- Pub Date:
- March 2021
- Quantitative Biology - Molecular Networks;
- Physics - Biological Physics
- 12 pages, 8 figures. Version 3, few more typos corrected, text further streamlined, corrected punctuations and minor mistakes in figure captions and main text, reduce white margin in figure 4