  Motivation: Inferring the structure of gene regulatory networks from
high--throughput datasets remains an important and unsolved problem. Current
methods are hampered by problems such as noise, low sample size, and incomplete
characterizations of regulatory dynamics, leading to networks with missing and
anomalous links. Integration of prior network information (e.g., from pathway
databases) has the potential to improve reconstructions.
  Results: We developed a semi--supervised network reconstruction algorithm
that enables the synthesis of information from partially known networks with
time course gene expression data. We adapted PLS-VIP for time course data and
used reference networks to simulate expression data from which null
distributions of VIP scores are generated and used to estimate edge
probabilities for input expression data. By using simulated dynamics to
generate reference distributions, this approach incorporates previously known
regulatory relationships and links the network to the dynamics to form a
semi-supervised approach that discovers novel and anomalous connections. We
applied this approach to data from a sleep deprivation study with KEGG pathways
treated as prior networks, as well as to synthetic data from several DREAM
challenges, and find that it is able to recover many of the true edges and
identify errors in these networks, suggesting its ability to derive posterior
networks that accurately reflect gene expression dynamics.
