Grass cell walls are an abundant component of natural ecosystems and have important practical uses as feed, fiber, and potential biofuel feedstocks. Phenylpropanoids, in the form of lignin and hydroxycinnamic acids, are a key constituent of grass cell walls. On the one hand, they reduce the accessibility of sugars in the wall; on the other, they increase the thermchemical conversion fuel quality relative to carbohydrates. This project is mining biological network data to develop a list of top candidate genes with previously uncharacterized roles in phenylpropanoid cell wall incorporation and regulation. The analysis uses three publicly available rice networks. Two are simple coexpression networks from Affymetrix array data, (1) that available through ROAD (www.ricearray.net) and based on simple Pearson’s correlation coefficient values, and (2) that available through PlaNet (http://aranet.mpimp-golm.mpg.de/) based on highest reciprocal ranking of correlation values. The third and potentially highest quality network is RiceNet (http://www.functionalnet.org/ricenet/), which combines protein-protein interaction and coexpression data from across eukaryotes with confidence levels expressed as log likelihood scores. When seeded with recently characterized grass cell wall phenylpropanoid modifying genes from the BAHD CoA acyltransferase family, we observe significant overlap (hypergeometric p < 0.02) between the networks. We are conducting separate analyses of the networks and developing a heuristic method to quantitatively combine them into a single network. In addition to small-scale reverse genetics validation, we provide an overview of plans to test the validity of the different networks through genome wide association analysis of cell wall quality in switchgrass.