  The inference of gene predictors in the gene regulatory network has become an
important research area in the genomics and medical disciplines. Accurate
predicators are necessary for constructing the GRN model and to enable targeted
biological experiments that attempt to confirm or control the regulation
process. In this paper, we implement a SAT-based algorithm to determine the
gene predictor set from steady state gene expression data (attractor states).
Using the attractor states as input, the states are ordered into attractor
cycles. For each attractor cycle ordering, all possible predictors are
enumerated and a CNF expression is formulated which encodes these predictors
and their biological constraints. Each CNF is explored using a SAT solver to
find candidate predictor sets. Statistical analysis of the results selects the
most likely predictor set of the GRN corresponding to the attractor data. We
demonstrate our algorithm on attractor state data from a melanoma study, and
present our predictor set results.
