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

Nature Methods volume 11, pages 197–202 (2014)Cite this article

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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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Figure 1: Modeling heterogeneous microscopy data.
Figure 2: Parameter and state inference using simulated measurements.
Figure 3: Reconstructed gene expression and promoter dynamics using simulated measurements.
Figure 4: Modeling and parameter inference for induced gene expression in yeast.
Figure 5: State reconstruction of heterogeneous reporter dynamics.
Figure 6: Sources of cell-to-cell variability in reporter expression.

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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.

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Author notes

  1. Heinz Koeppl

    Present address: Present address: TU Darmstadt, Darmstadt, Germany.,

Authors and 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

Authors
  1. Christoph Zechner
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  2. Michael Unger
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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.

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Correspondence to Heinz Koeppl.

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Zechner, C., Unger, M., Pelet, S. et al. Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings. Nat Methods 11, 197–202 (2014). https://doi.org/10.1038/nmeth.2794

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