Table 3.

Function of computational tools in identifying target gene for strain design.

Methodology	Description	Reference
BioPathway predictor	Identification non-native pathway by known chemical reactions and analysis according to various restrictions (e.g., maximum theoretical yield, pathway length, thermodynamic feasibility, etc).	Yim et al., 2011
BNICE	Identification novel pathways using a “generalized enzyme reaction” and evaluation by pathway length and thermodynamics of chemical formation	Hatzimanikatis et al., 2004
FSEOF	Identification gene amplification targets in response to an enforced objective flux of product formation on a genome-scale basis	Choi et al., 2010
MOMA	Prediction a metabolic phenotype of gene knock-out strain by minimizing a distance in flux space	Segre et al., 2002
OptForce	Prediction increase/decrease of a flux value to meet a pre-specific overproduction target	Ranganathan et al., 2010
OptGene	Prediction gene deletion targets to overproduce a desired product	Patil et al., 2005
OptKnock	Prediction gene deletion that maximizes target pathway flux	Burgard et al., 2003
OptORF	Prediction gene deletion or amplification targets by integrating transcriptional regulatory networks and metabolic networks	Kim and Reed, 2010
OptReg	Prediction deletion or amplification to overproduce a target product	Pharkya and Maranas, 2006
OptStrain	Prediction deletion or identification heterologous expression gene target to aid microbial strain design (pathway balancing, maximum product yield, optimal substrate and microbial host)	Pharkya et al., 2004
ROOM	Prediction knock-out strain metabolic fluxes at steady state by minimizing the number of significant flux changes.	Shlomi et al., 2005