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Cell Syst. 2015 Oct 28;1(4):270-82. doi: 10.1016/j.cels.2015.09.008. Epub 2015 Oct 22.

Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data.

Author information

1
Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland. Electronic address: gerosa@fas.harvard.edu.
2
Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland; Systems Biology Graduate School, Zurich 8057, Switzerland.
3
Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland.
4
Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland. Electronic address: sauer@imsb.biol.ethz.ch.

Abstract

Hundreds of molecular-level changes within central metabolism allow a cell to adapt to the changing environment. A primary challenge in cell physiology is to identify which of these molecular-level changes are active regulatory events. Here, we introduce pseudo-transition analysis, an approach that uses multiple steady-state observations of (13)C-resolved fluxes, metabolites, and transcripts to infer which regulatory events drive metabolic adaptations following environmental transitions. Pseudo-transition analysis recapitulates known biology and identifies an unexpectedly sparse, transition-dependent regulatory landscape: typically a handful of regulatory events drive adaptation between carbon sources, with transcription mainly regulating TCA cycle flux and reactants regulating EMP pathway flux. We verify these observations using time-resolved measurements of the diauxic shift, demonstrating that some dynamic transitions can be approximated as monotonic shifts between steady-state extremes. Overall, we show that pseudo-transition analysis can explore the vast regulatory landscape of dynamic transitions using relatively few steady-state data, thereby guiding time-consuming, hypothesis-driven molecular validations.

KEYWORDS:

computational biology; metabolism; metabolomics; regulation network; transcription factor

PMID:
27136056
DOI:
10.1016/j.cels.2015.09.008
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