Mechanistic links between cellular trade-offs, gene expression, and growth

Andrea Weisse, Diego A. Oyarzun, Vincent Danos, Peter Swain*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.
Original languageEnglish
Pages (from-to)E1038-E1047
Number of pages10
JournalProceedings of the National Academy of Sciences (PNAS)
Volume112
Issue number9
Early online date18 Feb 2015
DOIs
Publication statusPublished - 3 Mar 2015

Keywords / Materials (for Non-textual outputs)

  • Evolutionarily stable strategy
  • Host-circuit interactions
  • Mathematical cell model
  • Synthetic biology
  • Systems biology

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