A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics
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Date
2019-01-23
Publication Type
Journal Article
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yes
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Abstract
Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.
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published
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Journal / series
Cell Systems
Volume
8 (1)
Pages / Article No.
15 - 2600000000000
Publisher
Elsevier
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Software
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Subject
Cell-to-cell variability; non-linear mixed-effects models; Network inference; Time-lapse imaging; systems biology; endocytosis
Organisational unit
03699 - Stelling, Jörg / Stelling, Jörg