Optimally Orienting Physical Networks

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Inferring host gene subnetworks involved in viral replication

Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.

Optimally orienting physical networks

In a network orientation problem, one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This NP-complete problem arises in the context of analyzing physical networks of protein-protein and protein-DNA interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ILP formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified algorithmic variants that do not use information on edge directions. The obtained orientations can lead to a better understanding of the structure and function of the network.