Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of high-throughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the time-dependent propagation of active and passive states depending on a network structure. Optimal network structures given the experimental data are reconstructed. Two network inference algorithms can be used: inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA, a Genetic Algorithm network optimisation. Inclusion of prior biological knowledge can be done using different network prior models. Software Depends: R (>= 2.10.0), genefilter, gam, lattice, coda, gplots, graph, igraph, RBGL Suggests: multicore, Rgraphviz Christian Bender <christian.bender@tron-mainz.de> Christian Bender GPL (>= 2) 2012-04-16 application/tgz http://CRAN.R-project.org/package=ddepn