Essential genes consist of genes whose individual deletion is lethal (i.e., no biomass formation) under a specific environmental condition. By analogy, synthetic lethals (SL) refer to pairs of non-essential genes whose simultaneous deletion is lethal. The availability of genome-scale metabolic models of organisms has provided the foundation for the development of computational frameworks to rapidly predict the effect of multiple genetic manipulations on the strain’s growth phenotype under different media.

SL Finder is a targeted enumeration procedure for identification of synthetic lethal genes or reactions using genome-scale metabolic models. It relies on the solution of a bilevel optimization framework that utilizes flux balance analysis to identify all multi-reaction/gene lethals. The user needs to first specify a parameter n, indicating the order of synthetic lethals. For example, n = 2 corresponds to synthetic lethal pairs, n = 3 corresponds to synthetic lethal triples, etc. This bilevel formulation then identifies the set of n gene/reaction deletions that minimizes the maximum biomass formation potential of the network. If the minimal value of the maximum biomass is found to be below a pre-specified viability threshold (e.g., one percent of maximum biomass) then the corresponding combination of n gene/reaction deletions forms a SL. All alternative SL gene/reaction sets of size n are successively obtained by excluding the previously identified SLs using integer cuts and resolving the bilevel formulation.

Related Publications:

Suthers P.F., A. Zomorrodi, C.D. Maranas (2009), "Genome-scale Gene/Reaction Essentiality and Synthetic Lethality Analysis," Molecular Systems Biology, 5:301.

Interested in finding out the biomass precursors that are not produced upon essential (synthetic lethal) gene deletion? Check out the Precursor Identifier algorithm.

(Chowdhury R., Chowdhury A., Maranas C.D. (2015), "Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models", Metabolites, 5(4):536-570. PMID: 26426067.)

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