Scalable and flexible inference framework for stochastic dynamic single-cell models
Journal article, 2022

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

Sebastian Persson

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Niek Welkenhuysen

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Sviatlana Shashkova

University of Gothenburg

Sahlgrenska University Hospital

Samuel Wiqvist

Lund University

Patrick Philipp Reith

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Gregor W. Schmidt

Swiss Federal Institute of Technology in Zürich (ETH)

Umberto Picchini

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Marija Cvijovic

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 18

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Mathematical Analysis

DOI

10.1371/journal.pcbi.1010082

PubMed

35588132

More information

Latest update

7/17/2024