Computation of Single-Cell Metabolite Distributions Using Mixture Models

Mona K. Tonn, Philipp Thomas, Mauricio Barahona, Diego A. Oyarzún

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Article number614832
Number of pages11
JournalFrontiers in Cell and Developmental Biology
Publication statusPublished - 22 Dec 2020

Keywords / Materials (for Non-textual outputs)

  • metabolic variability
  • , stochastic gene expression
  • metabolic modeling
  • single-cell modeling
  • mixture model analysis


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