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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).