Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design

Table 2 Information on resulting strains after running the algorithm for the ethanol model.

	Equal-Weighted			Thermodynamically-Weighted
Fitness function^a	Ethanol	Biomass	Ethanol × Biomass	Ethanol	Biomass	Ethanol × Biomass
Number of reactions removed	13	10	18	11	10	11
Number of associated genes (or gene clusters) ^b	16	8	18	12	10	9
Gene list	aceA ackAB atpABCDEFGHI cyoABCD fbp frdABCD glpX ldhA mdh nuoAHJKLMNEFGBC pflB poxB pta talAB tdcE tktAB	aceA aceEF ldhA lpdA mdh pgi pntAB tktAB	adk atpABCDEFGHI cyoABCD eda fbp frdABCD glpX ldhA maeB mdh ndh pckA pflB poxB pps pta sfcA tdcE	atpABCDEFGHI cyoABCD fbaAB gnd maeB mdh ndh poxB pta rpe sfcA sucCD	atpABCDEFGHI cyoABCD frdABCD maeB pgmA pgml pntAB sfcA tktAB ytjC	aceA adk atpABCDEFGHI cyoABCD eda ndh pntAB pta sucCD
Total number of EMs	23	23	23	16	17	24
Biomass yield (fraction of theoretical)	0.000	0.839	0.456	0.000	0.861	0.483
Ethanol yield (fraction of theoretical)	1.000	0.007	0.584	1.000	0.003	0.499

^a Fitness functions are defined in the Methods section. ^b Enzymes with multiple subunits encoded by different genes in the same operon are only counted once.)

ISSN: 1752-0509