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bioRxiv · 10.1101/341867

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

Dharmarajan, L.·Kaltenbach, H.-M.·Rudolf, F.·Stelling, J.
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Abstract

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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Dharmarajan, L., Kaltenbach, H.-M., Rudolf, F., Stelling, J.. 2018-06-08. A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics. https://doi.org/10.1101/341867

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