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WIREs Syst Biol Med
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Microbial metabolic noise

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Abstract From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that “look” identical is extremely challenging. Since then, cell‐to‐cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell‐to‐cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering
(a) Cartoon view of the series of metabolic reactions in a form of a network that enable nutrient uptake and sustain the synthesis or polymerization of biomass building blocks and the provision of energy. (b) A magnified, coarse‐grain, view an isolated node of the metabolic network that converts metabolite Mi to metabolite Mj. This conversion is catalyzed by a specific enzyme the abundance and activity of which determines the metabolic flux (or rate of conversion). Green arrows denote potential allosteric regulation of enzyme activity by the metabolites themselves; red arrow denotes the genetic and epigenetic determinants of enzyme availability and abundance
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Cartoon representation of various elements pertaining to the stochastic modeling of metabolism, including: (a) transcriptional and translational bursting via random promoter switching; (b) stochastic Michaelis Menten kinetics that combine the effects of cellular noise at the gene expression with those at the metabolic reaction level; (c) negative feedback mechanisms, such as growth‐mediated coupling that can alter enzyme levels via synthesis and dilution creating a feedback loop; and (d) the substrate arrival kinetics
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Spontaneous metabolic dynamics generally yield cell‐to‐cell metabolic variability, namely isogenic cells utilizing alternative metabolic pathways (color‐coded). This variability can act as a potential strategy of phenotype rescue upon a nutrient shift. In this context, the new conditions select for a subpopulation using pathways that allow for uninterrupted growth in the new environment, thus, conferring an overall fitness advantage to the population
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Noise propagation in metabolic networks, namely: how temporal fluctuations in the abundance of metabolite α at a certain node (in red) propagates into the metabolic network imposing fluctuations in metabolite β. Two possible outcomes of noise propagation are presented, namely: (1) noise dissipation (in purple); and (2) noise propagation and related effects of amplitude and phase (in blue)
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