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.