We present a new distributed computing algorithm, Parallel Pattern Discovery (PPD), for constrained Non-negative Matrix Factorization (NMF). Our implementation offers the ability to constrain a specific pattern for optimization of the data while minimizing reconstruction error. Parallel Pattern Discovery operates within a distributed environment using a message passing interface. Distribution of the PPD algorithm provides better scalability and allows operation in single- or multiple-system environments. The algorithm was tested on a set of time-series, dose-dependent mRNA gene expression data. Parallel Pattern Discovery was found to accurately identify patterns within the data and reconstruct the original matrices. Our NMF algorithm found a smaller reconstruction error when compared against standard NMF algorithms. Development focused on running PPD as part of a system which identifies significantly contributing genes. Parallel Pattern Discovery is first run to find patterns from biological data. It is followed by Gene Set Enrichment (GSE) which takes the pattern data and relates it back to genetic pathways.