In this section
@article{Tonn:2019:10.1101/522425,
author = {Tonn, M and Thomas, P and Barahona, M and Oyarzun, D},
doi = {10.1101/522425},
journal = {Communications Biology},
title = {Stochastic modelling reveals mechanisms of metabolic heterogeneity},
url = {http://dx.doi.org/10.1101/522425},
volume = {2},
year = {2019}
}
TY - JOUR
AB - 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.
AU - Tonn,M
AU - Thomas,P
AU - Barahona,M
AU - Oyarzun,D
DO - 10.1101/522425
PY - 2019///
SN - 2399-3642
TI - Stochastic modelling reveals mechanisms of metabolic heterogeneity
T2 - Communications Biology
UR - http://dx.doi.org/10.1101/522425
UR - https://www.biorxiv.org/content/10.1101/522425v2
UR - http://hdl.handle.net/10044/1/67456
VL - 2
ER -