Automatic generation of causal networks linking growth factor stimuli to functional cell state changes

Abstract

Despite the increasing number of growth factor-related signalling networks, their lack of logical and causal connection to factual changes in cell states frequently impairs the functional interpretation of microarray data. We present a novel method enabling the automatic inference of causal multi-layer networks from such data, allowing the functional interpretation of growth factor stimulation experiments using pathway databases. Our environment of evaluation was hepatocyte growth factor-stimulated cell migration and proliferation in a keratinocyte-fibroblast co-culture. The network for this system was obtained by applying the steps: (a) automatic integration of the comprehensive set of all known cellular networks from the Pathway Interaction Database into a master structure; (b) retrieval of an active-network from the master structure, where the network edges that connect nodes with an absent mRNA level were excluded; and (c) reduction of the active-network complexity to a causal subnetwork from a set of seed nodes specific for the microarray experiment. The seed nodes comprised the receptors stimulated in the experiment, the consequently differentially expressed genes, and the expected cell states. The resulting network shows how well-known players, in the context of hepatocyte growth factor stimulation, are mechanistically linked in a pathway triggering functional cell state changes. Using BIOQUALI, we checked and validated the consistency of the network with respect to microarray data by computational simulation. The network has properties that can be classified into different functional layers because it not only shows signal processing down to the transcriptional level, but also the modulation of the network structure by the preceeding stimulation. The software for generating computable objects from the Pathway Interaction Database database, as well as the generated networks, are freely available at: http://www.tiga.uni-hd.de/supplements/inferringFromPID.html.