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Computation of Single-Cell Metabolite Distributions Using Mixture Models
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783310/ https://www.ncbi.nlm.nih.gov/pubmed/33415109 http://dx.doi.org/10.3389/fcell.2020.614832 |
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author | Tonn, Mona K. Thomas, Philipp Barahona, Mauricio Oyarzún, Diego A. |
author_facet | Tonn, Mona K. Thomas, Philipp Barahona, Mauricio Oyarzún, Diego A. |
author_sort | Tonn, Mona K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7783310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77833102021-01-06 Computation of Single-Cell Metabolite Distributions Using Mixture Models Tonn, Mona K. Thomas, Philipp Barahona, Mauricio Oyarzún, Diego A. Front Cell Dev Biol Cell and Developmental Biology 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. Frontiers Media S.A. 2020-12-22 /pmc/articles/PMC7783310/ /pubmed/33415109 http://dx.doi.org/10.3389/fcell.2020.614832 Text en Copyright © 2020 Tonn, Thomas, Barahona and Oyarzún. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Tonn, Mona K. Thomas, Philipp Barahona, Mauricio Oyarzún, Diego A. Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title | Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title_full | Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title_fullStr | Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title_full_unstemmed | Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title_short | Computation of Single-Cell Metabolite Distributions Using Mixture Models |
title_sort | computation of single-cell metabolite distributions using mixture models |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783310/ https://www.ncbi.nlm.nih.gov/pubmed/33415109 http://dx.doi.org/10.3389/fcell.2020.614832 |
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