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Ensemble modeling for analysis of cell signaling dynamics

Nature Biotechnology volume 25, pages 1001–1006 (2007)Cite this article

Abstract

Systems biology iteratively combines experimentation with mathematical modeling. However, limited mechanistic knowledge, conflicting hypotheses and scarce experimental data severely hamper the development of predictive mechanistic models in many areas of biology. Even under such high uncertainty, we show here that ensemble modeling, when combined with targeted experimental analysis, can unravel key operating principles in complex cellular pathways. For proof of concept, we develop a library of mechanistically alternative dynamic models for the highly conserved target-of-rapamycin (TOR) pathway of Saccharomyces cerevisiae. In contrast to the prevailing view of a de novo assembly of type 2A phosphatases (PP2As), our integrated computational and experimental analysis proposes a specificity factor, based on Tap42p-Tip41p, for PP2As as the key signaling mechanism that is quantitatively consistent with all available experimental data. Beyond revising our picture of TOR signaling, we expect ensemble modeling to help elucidate other insufficiently characterized cellular circuits.

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Figure 1: S. cerevisiae TOR signal transduction and workflow for its model-based analysis.
Figure 2: Model discrimination and identification of pivotal experiments.
Figure 3: Model-based hypothesis generation and experimental validation.
Figure 4: Logic of TOR signaling.

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Acknowledgements

We thank Michael N. Hall, Francis J. Doyle III and Joachim Buhmann for critical reading of the manuscript and helpful suggestions as well as the Swiss National Science Foundation and ETH Zurich for financial support to M.P.

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Author notes

  1. Lars Kuepfer

    Present address: Present address: Bayer Technology Services, 51368 Leverkusen, Germany.,

Authors and Affiliations

  1. Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8092, Switzerland

    Lars Kuepfer & Uwe Sauer

  2. Institute of Computational Science, ETH Zurich, Zurich, 8092, Switzerland

    Lars Kuepfer & Jörg Stelling

  3. Institute of Biochemistry, ETH Zurich, Zurich, 8092, Switzerland

    Matthias Peter

  4. Competence Center for Systems Physiology and Metabolic Disease, ETH Zurich, Zurich, 8092, Switzerland

    Matthias Peter, Uwe Sauer & Jörg Stelling

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  1. Lars Kuepfer
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Correspondence to Jörg Stelling.

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Kuepfer, L., Peter, M., Sauer, U. et al. Ensemble modeling for analysis of cell signaling dynamics. Nat Biotechnol 25, 1001–1006 (2007). https://doi.org/10.1038/nbt1330

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