Comparison of the flux distributions between before (r) and after (r’) pathway modification suggests an approach to redirect flux distribution for increasing ethanol productivity. Fig. 3.3(a) shows r and r’ at a specific instant when cell density is 3g/L. Metabolic shift caused by the genetic change is also presented by displaying the difference between r and r’ (Fig. 3.3(b)). Then, amplification of the mode throughput flux could be translated as amplification of a set of reactions with positive values of r’- r which are highlighted in colors in Figs. 3.3(a) and (b). From this analysis, we obtain several interesting findings as follows:

i. First of all, it is observed that none of the reactions in the glycolytic pathway are amplified. It implies that amplification of the glycolytic enzymes may not be a key to increasing ethanol productivity. This is consistent with experimental findings reported in the literature. Overproduction of different glycolytic enzymes of S. cerevisiae showed no effect on the rate of ethanol formation (Schaaff et al., 1989). It is because flux control is not inside the glycolytic pathway. Understandably, past efforts for increasing the glycolytic flux by overproduction of glycolytic enzymes have been often unsuccessful (Koebmann et al., 2002). In the in silico analysis, flux control is found elsewhere (highlighted in color) which includes xylose utilization pathway, and pentose phosphate (PP) pathway.

ii. While recombinant strain 1400 (pLNH33) efficiently utilizes xylose through the pathway constructed by overexpressing exogenous genes (XR and XDH), as well as endogenous gene (XK), simulation shows that the increase of ethanol productivity requires further overexpression of not only xylose transport reactions (i. e., R31 and R32), but also xylitol conversion to X5P (i. e., R34 and R35).

iii. In addition, it is shown that four reactions in the PP pathway (R19 to R22), i. e., transaldolase (TAL1), transketolase (TKL1), ribulose-5-phosphate 4-epimerase (RPE1) and ribulokinase (RKI1), are possible targets for overexpression. The finding by Johansson & Hahn-Hagerdal (2002) that overexpression of all four genes resulted in better ethanol production than the overexpression of each gene individually is also consistent with the simulation result.

iv. Another interesting aspect that emerges from the model is as follows. Jeppsson et al. (2002) observed that deletion of ZWF1 (i. e., R17), coding for glucose-6-phosphate dehydrogenase, results in higher ethanol yield but lower productivity. Instead, the hybrid model shows the need to overexpress this oxidative PP pathway to increase ethanol productivity. The calculations show an increase in productivity though there is a small drop in the yield.