Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability. Author summaryUnderstanding the causes of heterogeneity and the means by which it can be controlled is crucial for manipulating cellular populations and treating diseases. To this end, single-cell time-lapse microscopy data is often combined with dynamic modelling. However, the construction of mechanistic models requires the ability to infer unknown model quantities from data, while simultaneously accounting for intrinsic and extrinsic noise. Here we propose a Bayesian inference framework which enabled us to elucidate sources of cell-to-cell variability in yeast signalling and provides deeper insights into the causes and consequences of heterogeneity. Our approach is versatile and can for example further be applied in pharmacokinetic and pharmacodynamic studies, epidemic studies, as well when modelling the behaviour of cancer cell populations.

Automated summary

Understanding the causes and consequences of heterogeneity in cellular populations is crucial for disease treatment and population manipulation. In this study, we propose a Bayesian inference framework to elucidate sources of cell-to-cell variability in yeast signaling, providing deeper insights into the causes and consequences of heterogeneity.