Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of Flux Balance Analysis (FBA), extending it either to exhaustively analyse all possible combinations of genes or formulate the problem as a bi-level Mixed Integer Linear Programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.

Results: We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster.We also parallelised our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under six hours. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.

Availability: The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0 is available at https://github.com/RamanLab/FastSL

Contact: kraman@iitm.ac.in

Supplementary Information: Supplementary data are available online.