Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.