Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects

BMC Systems Biology

Table 2 The optimal solution for maximizing ethanol productivity by S. cerevisiae considering resilience effects

ε-value	$v_{P Y K}^{*} ∕ v_{P Y K}^{b a s a l}$	Modulated enzymes
1	1.482	HXT
2	1.710	HXT, TDH
	1.618^†	HXT, PFK
	1.519^†	HXT, ATPase
3	1.991	HXT, TDH, ATPase
	1.877^†	HXT, TDH, PFK
	1.663^†	HXT, PFK, PYK
4	2.340	HXT, TDH, PFK, PYK
5	2.741	HXT, TDH, PFK, PYK, GLK
6	3.080	HXT, TDH, PFK, PYK, GLK, ATPase
7	3.106	HXT, TDH, PFK, PYK, GLK, ATPase, GOL
8	3.105	HXT, TDH, PFK, PYK, GLK, ATPase, GOL, TPS

The optimal enzymatic modulation for maximizing ethanol productivity by S. cerevisiae considering cell viability and metabolic adjustment for $[ζ_{x ∕ e}^{L B}, ζ_{x ∕ e}^{U B}] = [1.6, 2.0]$ . $γ_{x_{i}}^{L B}$ and $γ_{e_{i}}^{L B}$ are set to 0.2. $γ_{x_{i}}^{U B}$ and $γ_{e_{i}}^{U B}$ are set to 5.0. The superscript * means optimal solution, † indicates that the optimal result is obtained by solving fuzzy optimization problem using the fixed enzymatic modulations, and ε is the number of allowed manipulated genes.

ISSN: 1752-0509