Metabolomics evaluates the fluctuations between entire sets of metabolites in a given system. This form of research provides a look at the effects of environmental or genetic conditions on the natural metabolic state of an organism. To effectively identify these effects, a well-characterized metabolic model of an organism is often necessary to map the changes on to. This makes it difficult to study uncharacterized organisms. Alternatively, metagenomics could be used to create a metabolic model of uncharacterized systems that could be used for evaluation. PyFBA is a flux balance analysis program that can predict a metabolic model. This project has two main goals: 1) to make a program that can take metabolomic data and identify the metabolites present based on a predicted metabolic model; 2) to evaluate the accuracy of the model created by PyFBA and to improve the annotations of genes of unknown functions. Within two runs MS_FBA identified 290 features that were matched to 286 compounds from a list of 699 detectable, predicted compounds from PyFBA. The same experiments were compared to the ModelSEED database and resulted in 1243 features that were matched to 5,225 potential compounds from a list of 27,693 total compounds.