Abstract
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.
Highlights
Non-genetic heterogeneity is a hallmark of cell physiology
Cellular metabolism has been regarded as a deterministic process on the basis that metabolites appear in large numbers that filter out stochastic phenomena (Heinemann and Zenobi, 2011)
Recent data shows that single-cell metabolite distributions can display substantial heterogeneity in their abundance across single cells (Bennett et al, 2009; Imamura et al, 2009; Lemke and Schultz, 2011; Paige et al, 2012; Ibáñez et al, 2013; Yaginuma et al, 2014; Esaki and Masujima, 2015; Xiao et al, 2016; Mannan et al, 2017)
Summary
Non-genetic heterogeneity is a hallmark of cell physiology. Isogenic cells can display markedly different phenotypes as a result of the stochasticity of intracellular processes and fluctuations in environmental conditions. In particular, has received substantial attention thanks to robust experimental techniques for measuring transcripts and proteins at a single-cell resolution (Golding et al, 2005; Taniguchi et al, 2010) This progress has gone hand-inhand with a large body of theoretical work on stochastic models to identify the molecular processes that affect expression heterogeneity (Swain et al, 2002; Raj and van Oudenaarden, 2008; Thomas et al, 2014; Dattani and Barahona, 2017; Tonn et al, 2019). Our theory provides a quantitative basis to draw testable hypotheses on the sources of metabolite heterogeneity, which together with the ongoing efforts in singlecell metabolite measurements, will help to re-evaluate the role of metabolism as an active source of phenotypic variation
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