Ensemble modeling for analysis of cell signaling dynamics

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...

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Veröffentlicht in:Nature biotechnology 2007-09, Vol.25 (9), p.1001-1006
Hauptverfasser: Stelling, Jörg, Kuepfer, Lars, Peter, Matthias, Sauer, Uwe
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container_title Nature biotechnology
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creator Stelling, Jörg
Kuepfer, Lars
Peter, Matthias
Sauer, Uwe
description 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.
doi_str_mv 10.1038/nbt1330
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subjects Biotechnology
Cellular biology
Experimental data
Mathematical models
Models, Biological
Protein-Serine-Threonine Kinases
Reproducibility of Results
Saccharomyces cerevisiae
Saccharomyces cerevisiae - cytology
Saccharomyces cerevisiae - metabolism
Saccharomyces cerevisiae Proteins - metabolism
Signal Transduction
title Ensemble modeling for analysis of cell signaling dynamics
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