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Nat Methods. 2014 Feb;11(2):197-202. doi: 10.1038/nmeth.2794. Epub 2014 Jan 12.

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.

Author information

1
Automatic Control Lab, ETH Zurich, Zurich, Switzerland.
2
1] Automatic Control Lab, ETH Zurich, Zurich, Switzerland. [2] Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
3
Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
4
Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
5
1] Automatic Control Lab, ETH Zurich, Zurich, Switzerland. [2] IBM Zurich Research Laboratory, Rueschlikon, Switzerland. [3].

Abstract

Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.

PMID:
24412977
DOI:
10.1038/nmeth.2794
[Indexed for MEDLINE]

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