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-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on 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 of metabolite heterogeneity.
Issue Date: 29-Jan-2019
URI: http://hdl.handle.net/10044/1/67456
DOI: https://doi.org/10.1038/s42003-019-0347-0
Publisher: bioRxiv
Replaces: 10044/1/67454
http://hdl.handle.net/10044/1/67454
Copyright Statement: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Royal Commission for the Exhibition of 1851
Human Frontier Science Program
Funder's Grant Number: EP/N014529/1
RGY-0076/2015
Keywords: Science & Technology
Life Sciences & Biomedicine
Biology
Multidisciplinary Sciences
Life Sciences & Biomedicine - Other Topics
Science & Technology - Other Topics
GENE-EXPRESSION
PRINCIPLES
KINETICS
NETWORK
GROWTH
q-bio.MN
q-bio.MN
Publication Status: Published
Embargo Date: publication subject to indefinite embargo
Open Access location: https://doi.org/10.1038/s42003-019-0347-0
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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