Piyush Labhsetwar, Marcelo C. R. Melo, John A. Cole, and Zaida Luthey-Schulten.
Population FBA predicts metabolic phenotypes in yeast.
PLoS Computational Biology, 13:e1005728, 2017.
(PMC: PMC5626512)
LABH2017-ZLS
Using protein counts sampled from single cell proteomics distributions to
constrain fluxes through a genome-scale model of metabolism, Population
flux balance analysis (Population FBA) successfully described metabolic
heterogeneity in a population of independent Escherichia coli cells growing
in a defined medium. We extend the methodology to account for
correlations in protein expression arising from the co-regulation of genes
and apply it to study the growth of independent Saccharomyces cerevisiae
cells in two different growth media. We find the partitioning of flux between
fermentation and respiration predicted by our model agrees with recent
13C fluxomics experiments, and that our model largely recovers the
Crabtree effect (the experimentally known bias among certain yeast
species toward fermentation with the production of ethanol even in the
presence of oxygen), while FBA without proteomics constraints predicts
respirative metabolism almost exclusively. The comparisons to the 13C
study showed improvement upon inclusion of the correlations and
motivated a technique to systematically identify inconsistent kinetic
parameters in the literature. The minor secretion fluxes for glycerol and
acetate are underestimated by our method, which indicate a need for
further refinements to the metabolic model. For yeast cells grown in
synthetic defined (SD) medium, the calculated broad distribution of growth
rates matches experimental observations from single cell studies, and we
characterize several metabolic phenotypes within our modeled populations
that make use of diverse pathways. Fast growing yeast cells are predicted
to perform significant amount of respiration, use serine-glycine cycle and
produce ethanol in mitochondria as opposed to slow growing cells. We use
a genetic algorithm to determine the proteomics constraints necessary to
reproduce the growth rate distributions seen experimentally. We find that a
core set of 51 constraints are essential but that additional constraints are
still necessary to recover the observed growth rate distribution in SD
medium.
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