Computation of single-cell metabolite distributions using mixture models

TL;DR

This paper introduces a novel method to predict single-cell metabolite distributions using Gaussian mixture models derived from enzyme expression data, addressing the challenge of understanding metabolic heterogeneity.

Contribution

It presents a general, efficient approach to estimate metabolite distributions without extensive stochastic simulations, linking enzyme expression to metabolic variability.

Findings

Metabolite distributions can be modeled as Gaussian mixtures.

The method enables prediction of how biochemical parameters influence heterogeneity.

It provides a systematic framework for studying metabolic variability in disease.

Abstract

Metabolic heterogeneity is widely recognised 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.