The conversion of pentose to ethanol is one of the major barriers of industrializing the lignocellulosic ethanol processes. As one of the most promising native strains for pentose fermentation, Scheffersomyces stipitis (formerly known as Pichia stipitis) has been widely studied for its xylose fermentation. In spite of the abundant experimental evidence regarding ethanol and byproducts production under various aeration conditions, the mathematical descriptions of the processes are rare. In this work, a constraint-based metabolic network model for the central carbon metabolism of S. stipitis was reconstructed by integrating genomic (S. stipitis v2.0, KEGG), biochemical (ChEBI, PubChem), and physiological information available for this microorganism and other related yeast. The model consists of the stoichiometry of metabolic reactions, biosynthetic requirements for growth, and other constraints. Flux balance analysis is applied to characterize the phenotypic behavior of S. stipitis grown on xylose. The model predictions are in good agreement with published experimental results. To understand the effect of redox balance on xylose fermentation, we propose a system identification-based metabolic analysis framework to extract biological knowledge embedded in a series of designed in silico experiments. In the proposed framework, we first design in silico experiments to perturb the metabolic network in order to investigate the interested properties and then perform system identification, whereby applying principal component analysis (PCA) to the data generated by the designed in silico experiments. By combining the in silico perturbation experiments with system identification tools, biologically meaningful information contained in the complex network structure can be decomposed and translated into easily interpretable information that is useful for biologist. The PCA analysis identifies the phenotypic changes caused by oxygen supply and reveals key metabolic reactions related to redox homeostasis in different phenotypes. In addition, the influence of the cofactor preference of key enzyme (xylose reductase) in xylose metabolism is investigated using the proposed approach, and the results provide important insights on cofactor engineering of xylose metabolism.