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Over the past decade, genome-scale metabolic models have gained widespread acceptance in biology and bioengineering as a means of quantitatively predicting organism behavior based on the stoichiometry of the biochemical reactions constituting the organism metabolism. The list of applications for these models is rapidly growing; they have been used to identify essential genes, determine growth conditions, predict phenotypes, predict response to mutation, and study the impact of transcriptional regulation on organism phenotypes. This growing field of applications, combined with the rapidly growing number of available genome- scale models, is producing a significant demand for computation to analyze these models. Here we discuss how high-performance computing may be applied with various algorithms for the reconstruction, analysis, and optimization of genome-scale metabolic models. We also performed a case study to demonstrate how the algorithm for simulating gene knockouts scales when run on up to 65,536 processors on Blue Gene/P. In this case study, the knockout of every possible combination of one, two, three, and four genes was simulated in the iBsu1103 genome-scale model of B. subtilis. In 162 minutes, 18,243,776,054 knockouts were simulated on 65,536 processors, revealing 288 essential single knockouts, 78 essential double knockouts, 99 essential triple knockouts, and only 28 essential quadruple knockouts.

Application of high-performance computing to the reconstruction, analysis, and optimization of genome-scale metabolic models