Stochastic modelling reveals mechanisms of metabolic heterogeneity

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Title: Stochastic modelling reveals mechanisms of metabolic heterogeneity
Authors: Tonn, M
Thomas, P
Barahona, M
Oyarzun, D
Item Type: Journal Article
Abstract: Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cellphenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomicand proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Herewe propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations inenzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolicallydistinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodalexpression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Basedon published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought.Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources ofmetabolite heterogeneity.
Issue Date: 21-Mar-2019
Date of Acceptance: 7-Feb-2019
URI: http://hdl.handle.net/10044/1/67456
ISSN: 2399-3642
Publisher: Nature Research (part of Springer Nature)
Journal / Book Title: Communications Biology
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Royal Commission for the Exhibition of 1851
Funder's Grant Number: EP/N014529/1
Publication Status: Accepted
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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