  Synthetic lethal reaction/gene-sets are sets of reactions/genes where only
the simultaneous removal of all reactions/genes in the set abolishes growth of
an organism. In silico, synthetic lethal sets can be identified by simulating
the effect of removal of gene sets from the reconstructed genome-scale
metabolic network of an organism. Flux balance analysis (FBA), based on linear
programming, has emerged as a powerful tool for the in silico analyses of
metabolic networks. To identify all possible synthetic lethal reactions
combinations, an exhaustive sampling of all possible combinations is
computationally expensive. We surmount the computational complexity of
exhaustive search by iteratively restricting the sample space of reaction
combinations for search, resulting in a substantial reduction in the running
time. We here propose an algorithm, Fast-SL, which provides an efficient way to
analyse metabolic networks for higher order lethal reaction sets. Fast-SL
offers a substantial speed-up through a massive reduction in the search space
for synthetic lethals; in the case of E. coli, Fast-SL reduces the search space
for synthetic lethal triplets by over 4000-fold. Fast-SL also compares
favourably with SL Finder, an algorithm for identifying synthetic lethal sets,
by Suthers et al (2009), which involves the solution of a bi-level Mixed
Integer Linear Programming problem. We have implemented the Fast-SL algorithm
in MATLAB, building upon COBRA toolbox v2.0.
