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Table 1 Different deletion strategies suggested by OptGene algorithm for improving succinate yield and Biomass Product Coupled Yield.

From: Evolutionary programming as a platform for in silico metabolic engineering

Objective function

Number of deletions

Suggested deletions1

Objective function value2

%Maximum Growth

Unique solution?3

Succinate yield

5

SDH-complex, ZWF1, PDC6, U133, U221

0.39

14%

Yes

  

SDH-complex, ZWF1, PDC6, U133, U41

0.37

1%

Yes

 

4

SDH-complex, ZWF1, PDC6, AGP3

0.356

30%

Yes

 

3

SDH-complex, ZWF1, PFK2

0.211

4%

Yes

  

SDH-complex, SER3, THR1

0.074

76%

Yes

Succinate Biomass Product Coupled Yield

4

SDH-complex, ZWF1, PDC6, AGP3

29

30%

Yes

  

SDH-complex, SER3, THR1, U221

22

75%

Yes

 

3

SDH-complex, SER3, THR1

16

76%

Yes

  

SDH-complex, ZWF1, GLT1

9.78

42%

Yes

  1. 1 Only few of the suggested strategies, with high objective function values are shown. OptGene found many strategies with different, but high objective function values. This tendency can be controlled by varying GA parameters.
  2. 2 Units are: Yield in gram (gram glucose)-1, Biomass Product Coupled Yield in milli-gram (gram-glucose.hour)-1
  3. 3 Uniqueness of the solution was verified by first optimizing for the biomass, and then minimizing and maximizing the succinate flux at fixed, optimal biomass value.
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BMC Bioinformatics

ISSN: 1471-2105

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