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Proc Natl Acad Sci U S A. 2015 Mar 3;112(9):E1038-47. doi: 10.1073/pnas.1416533112. Epub 2015 Feb 18.

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

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SynthSys-Synthetic & Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom; School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom;
Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom; and.
SynthSys-Synthetic & Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom; School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom


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.


evolutionarily stable strategy; host–circuit interactions; mathematical cell model; synthetic biology; systems biology

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