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Table 6 Runtime analysis for the Berge algorithm and BIP using several examples from the literature with different design objectives

From: Comparison and improvement of algorithms for computing minimal cut sets

        Runtime (sec)
Organism Objective |D| |T| # MCS Min.Δ Max.Δ Berge BIP CNA
E. coli[16] (anaerobic) ethanol 12 4,998 1,048 6 9 0.011 0.287 2.83
E. coli[16] (aerobic) ethanol 12 429,264 55,488 11 15 0.883 2.174 547.61
E. coli[15] (anaerobic) isobutanol 7 5,615 760 7 10 0.011 0.233 2.69
E. coli[33] (anaerobic) n-butanol 7 7,341 2,280 7 10 0.015 0.226 3.43
  1. Both algorithms use all preprocessing procedures. For comparison we also use the program package CellNetAnalyzer[32] which uses a MATLAB script of the Berge algorithm. (Abbreviations: #MCS, number of MCS; min. Δ, minimal number of deletions; max. Δ, maximal number of deletions; CNA, CellNetAnalyzer).