||Metabolic pathways are analysed by either metabolic flux analysis (MFA) and/or metabolic control analysis (MCA) to identify enzyme(s) in the pathway that should be modified in order to improve cellular metabolism for engineering purposes. In this study, an MFA software package, Metstoich, was developed for metabolic analysis by connecting metabolism with practical engineering parameters such as biomass yield, etc. Metstoich allows the analysis of the metabolic pathway fluxes with the effect of, for example, variable mcromolecular composition and various extents of major catabolic pathways. This extensive analysis could be further used for practical applications. A metabolic pathway optimisation method, Hill-Climbing Optimisation Method (HCOM), was proposed in this study. HCOM is a simple iterative optimisation algorithm with using either MCA or the actual flux increased with the overproduced of individual enzymes. Compared to existing methods, HCOM can give better results in enhanced flux vs. enzyme-overproduction ratio, and tends to locate a few key enzymes along the metabolic pathway to be modified with suggested overproduction ratios. HCOM was applied to an idealised metabolic pathway and shows that the pathway flux could be increased up to 117% and 90% against doubling the total enzyme concentrations, with using the actual flux increase and MCA for decision making in every iteration loop of optimisation process. HCOM was also applied for optimising yeast glycolytic pathway. Two similar models, with the same experimental determined central glycolytic pathway, were optimised: (1) the simplified model only with glycolysis, and (2) the comprehensive bioreactor model with glycolysis and cell growth, etc. Both models give different enzyme overproduction outcomes. This is due to differences in pathway structure in the model and energetics. Energy is net generated in glycolysis to support cellular activities. Energy consumption is an important key for glycolysis optimisation, even through such activity is not part of the glycolysis. The energy demand is fixed in the simple model, but changes as cellular conditions change in the comprehensive model. Such model characteristics lead to the difference in optimisation outcomes.