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

Abstract : 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. systems biology | synthetic biology | mathematical cell model | host-circuit interactions | evolutionarily stable strategy I ntracellular processes rarely work in isolation but continually interact with the rest of the cell. Yet often we study cellular processes with the implicit assumption that the remainder of the cell can either be ignored or provides a constant, background environment. Work in both systems and synthetic biology is, however, showing that this assumption is weak, at best. In microbes, growth rate can affect the expression both of single genes (1, 2) and across the entire genome (3-6). Specific control by transcription factors seems to be complemented by global, unspecific regulation that reflects the physiological state of the cell (5-7). Correspondingly, progress in synthetic biology is limited by two-way interactions between synthetic circuits and the host cell that cannot be designed away (8, 9). These phenomena are thought to arise from trade-offs where commitment of a finite intracellular resource to one response necessarily reduces the commitment of that resource to another response. A trade-off in the allocation of ribosomes has been suggested to underlie global gene regulation (2, 5). Similarly, depletion of finite resources and competition for cellular processes is thought to explain the failure of some synthetic circuits (8). Such circuits "load" the host cell, which can induce physiological responses that further degrade the function of the circuit (10). Our understanding of such trade-offs, however, is mostly phenomenological. Here we take an alternative approach and ask what new insight can be gained from a minimal mechanistic model that captures these trade-offs. We focus on three trade-offs that can be considered universal in the sense that they are experienced by all living cells: (i) finite levels of cellular energy so that launching a new biochemical process reduces the activities of others; (ii) finite levels of ribosomes so that translating a new type of mRNA reduces translation of all other mRNAs; and (iii) a finite proteome, or cell mass, so that expressing a new type of protein reduces levels of other types. Reduced demand on any of these finite resources will, correspondingly, free that resource for other intracellular processes. We develop a mechanistic cellular model built around these three trade-offs. The model predicts allocation of the proteome, energy turnover, and physiological phenotypes, such as growth rate, from specifications made at the level of genotype, and thus connects molecular mechanisms to cellular behavior. A whole-cell model has been proposed as one way to make such predictions (11), but its level of detail may sometimes obscure the core biochemistry that underlies the observed phenotypes and potentially complicates further analyses. We instead adopt a complementary coarse-grained approach (12-14) and try to find minimal descriptions that highlight the mechanisms generating the in silico phenotypes we observe. In contrast to other approaches (13, 14), we emphasize that we do not optimize either growth rate or any other physiological variable. With only these trade-offs we can derive fundamental properties of microbial growth (15, 16) and potentially explain diverse phenomena such as gene dosage compensation (17) and host effects on the performance of synthetic circuits. Our mechanistic framework can be extended to include, for example, signal transduction and population-scale effects. Using such an extension , we study the evolutionary benefits of gene regulation and find that transcriptional regulation of metabolic pathways may Significance Cells have finite resources. Committing resources to one task therefore reduces the amount of resources available to others. These trade-offs are often overlooked but potentially modify all cellular processes. Building a mathematical cell model that respects such trade-offs and describes the mechanisms of protein synthesis and how cells extract resources from their environment, we quantitatively recover the typical behavior of an individual growing cell and of a population of cells. As trade-offs are experienced by all cells and because growth largely determines cellular fitness, a predictive understanding of how biochemical processes affect others and affect growth is important for diverse applications, such as the use of microbes for biotechnology, the inhibition of antibiotic resistance, and the growth of cancers.
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Submitted on : Thursday, January 10, 2019 - 9:03:44 AM
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Andrea Weiße, Diego Oyarzun, Vincent Danos, Peter Swain. Mechanistic links between cellular trade-offs, gene expression, and growth. Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2015, 112 (9), pp.E1038-E1047. ⟨10.1073/pnas.1416533112⟩. ⟨hal-01976394⟩



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