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Over the years, methods capable of integrating data from omics, such as transcriptomics, proteomics and metabolomics have emerged in Systems Biology, principally the use of networks to integrate omics information. In particular, the role of each biological pathway aims to understand the intermolecular interactions. While there are theoretical and experimental strategies to investigate biological pathways involved in cellular metabolism, computational modeling methods allow for a better understanding of them. Here we propose a new method to connect transcriptomic data with simulation approach using stochastic Petri Net (PN) metabolic networks. This new approach was developed based on well-studied theoretical gene expression modeling while trying to assimilate dynamic ordinary systems to a stochastic model function. The developed method was used to perform stochastic PN simulations of ethanol fermentation by Saccharomyces cerevisiae considering glucose and xylose as carbon sources. Lastly, we developed the PeNTIOS software, which is capable of converting Saccharomyces cerevisiae metabolic pathways and transcriptomic data into SBML format. The generated files can be readily imported into PN simulation programs. Our results show that the reconstruction of stochastic PN systems with transcriptomics data is a promising method that can generate new insights about biological experiments, as shown through our case study with the xylose-fermenting yeast. A Novel Method for the Integration of Stochastic Petri Net Simulation and Transcriptomic Data Applied to a Metabolic Pathway