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: Working Paper
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/10044/1/67454
DOI: https://dx.doi.org/10.1101/522425
Publisher: bioRxiv
Copyright Statement: © 2019 The Author(s). This preprint is made available under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). https://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Human Frontier Science Program
Engineering & Physical Science Research Council (EPSRC)
Royal Commission for the Exhibition of 1851
Funder's Grant Number: RGY-0076/2015
EP/N014529/1
Keywords: q-bio.MN
q-bio.MN
Publication Status: Published
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



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