Level 4: Process Description
ScFEA/FLUXestimator 41
Single cell flux estimation analysis (scFEA) is a prediction tool that
infers metabolic flux from scRNAseq data using hand-curated metabolic
pathways from KEGG as well as some hand curated mechanisms as prior
knowledge. In the web-application of scFEA, FLUXestimator, metabolic
pathways from Recon3d are also available.
scFEA constructs a reduced network based on the prior network topology,
genes with significant non-zero expression, and any preferred
sub-network specifications from the user. This reduced network, termed a
factor graph, is composed of metabolic modules (variables), representing
groups of connected reactions, linked by intermediate metabolites
(factors). For estimation, scFEA combines traditional flux-balance
analysis with an optimization goal of minimizing influx/outflux
imbalances while also incorporating enzyme transcript levels as a proxy
for enzyme activity to further constrain the model search space.
scFEA was validated experimentally using matched scRNAseq and targeted
metabolomics data collected from cells exposed to hypoxia and/or APEX1
knockdown. The authors observed that the predicted flux changes were
consistent with the observed changes in the metabolomics data.
Fast-SL 42
Fast-SL uses iterative search space reduction for rapid identification
of synthetic-lethal gene sets up to an order of four. The overarching
goal of this algorithm is to improve the computational efficiency and
speed of synthetic lethality prediction from large metabolic networks.
Because of its improved computational efficiency, Fast-SL is able to
predict higher order synthetic lethal gene sets.
For the deletion of a gene/reaction to be considered lethal, the maximum
growth calculated by flux balance analysis (FBA) must be smaller than
the specified cutoff (vco), typically 1% of the
wild-type growth rate. The algorithm calculates the lethality cutoff
vco as 1% of the ‘minimum norm’, which corresponds to
the maximum wild-type growth rate. Beginning with single lethal (first
order) reactions, the search space is constrained to all reactions in
the system with a nonzero flux in the distribution from the prior step.
These reactions are denoted Jnz. Reactions in
Jnz are then exhaustively tested for single-lethality by
setting the flux of each individual reaction to zero, calculating the
biomass flux, and comparing it to the cutoff, vco. If
the biomass flux is less than the cutoff, the reaction is considered
lethal and added to the set of single lethal reactions
(Jsl). Reactions in Jsl are then pruned
from the search space for double lethal (second order) reactions
(Jdb). When calculating third order lethal reactions,
the search space would be further reduced as reactions in
Jdb are removed from Jnz. The result is
an iteratively pruned search space which becomes smaller with increasing
order of lethal gene sets.
Using Fast-SL, the authors successfully identified lethal gene sets up
to an order of four in E. coli, S.Typhimurium, and M.
tuberculosis . They validated these results with an exhaustive search
for first, second, and third order lethal gene sets. The authors
reported an “exact match” between the number of lethal sets identified
in the exhaustive search and those identified by Fast-SL. The authors
also compared Fast-SL to another algorithm, SL Finder, which is also
intended to reduce the computational intensity of identifying synthetic
lethal gene sets. Fast-SL identified 127 novel triplets in E.
coli which were not found by SL Finder. These novel triplets were
predominantly involved in central carbon metabolism and amino acid
synthesis.