In this section
@article{Tonn:2020:10.3389/fcell.2020.614832,
author = {Tonn, MK and Thomas, P and Barahona, M and Oyarzún, DA},
doi = {10.3389/fcell.2020.614832},
journal = {Front Cell Dev Biol},
title = {Computation of Single-Cell Metabolite Distributions Using Mixture Models.},
url = {http://dx.doi.org/10.3389/fcell.2020.614832},
volume = {8},
year = {2020}
}
TY - JOUR
AB - Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
AU - Tonn,MK
AU - Thomas,P
AU - Barahona,M
AU - Oyarzún,DA
DO - 10.3389/fcell.2020.614832
PY - 2020///
SN - 2296-634X
TI - Computation of Single-Cell Metabolite Distributions Using Mixture Models.
T2 - Front Cell Dev Biol
UR - http://dx.doi.org/10.3389/fcell.2020.614832
UR - https://www.ncbi.nlm.nih.gov/pubmed/33415109
UR - http://hdl.handle.net/10044/1/85947
VL - 8
ER -