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

Nature Methods volume 11, pages 197202 (2014) | Download Citation

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.

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Acknowledgements

We want to thank H.R. Kuensch and J. Hasenauer for their valuable feedback on the manuscript and O. Aalen for providing us with his technical report from 1988. We thank F. Rudolf for help in designing and cloning the Y-Venus destabilized reporter and S. Lee with the fluidic setup. C.Z., M.U. and H.K. acknowledge support from the Swiss National Science Foundation, grant no. PP00P2_128503 and SystemsX.ch. S.P. and M.P. acknowledge support from the European project UNICELLSYS, European Research Council, SystemsX.ch organization (LiverX), Swiss National Science Foundation and ETH Zurich. M.U. receives support from the Life Science Zurich PhD Program on Systems Biology of Complex Diseases; and M.U., M.P. and H.K. acknowledge support from the Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland.

Author information

Author notes

    • Heinz Koeppl

    Present address: TU Darmstadt, Darmstadt, Germany.

Affiliations

  1. Automatic Control Lab, ETH Zurich, Zurich, Switzerland.

    • Christoph Zechner
    • , Michael Unger
    •  & Heinz Koeppl
  2. Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.

    • Michael Unger
    •  & Matthias Peter
  3. Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.

    • Serge Pelet
  4. IBM Zurich Research Laboratory, Rueschlikon, Switzerland.

    • Heinz Koeppl

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Contributions

C.Z., M.U., M.P. and H.K. designed the research; C.Z. and H.K. conceived of mathematical methods, performed simulations and analyzed data; M.U. and S.P. developed strains; M.U. and S.P. performed experiments and measured data; and C.Z. and H.K. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Heinz Koeppl.

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DOI

https://doi.org/10.1038/nmeth.2794

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