Ethanol production improvements give rise to the breeding of yeast strains, that became the investigation focus in several research centers. Recently, a wild strain used in Brazilian sugarcane industry was sequenced, the PE-2 strain of Saccharomyces cerevisiae, and this event brought an interest in studying the dynamics of the fermentation of this strain in order to understand which aspects this strain become more adapted to the fermentation conditions, maintaining a high capacity to produce bioethanol. From the analysis of transcriptional strain PE-2, we seek for inference networks methods that can represent the dynamics of this yeast.In this work, we model an experimental temporal data of strain PE-2 and strain S288c (used as a reference) based on Boolean networks model. In this model, the data are converted from continuous into discrete data (0 or 1) and, based on constraints rules of Boolean Network model, networks are inferred to represent gene interactions over time based on temporal data. We successfully model networks using a set with 11 and 12 genes related to yeast glycolysis and fermentation pathways.