Construction of Signaling Pathways with RNAi Data and Multiple Reference Networks

Signaling networks are involved in almost all major diseases such as cancer. As a result of this, understanding how signaling networks function is vital for finding new treatments for many diseases. Using gene knockdown assays such as RNA interference (RNAi) technology, many genes involved in these networks can be identified. However, determining the interactions between these genes in the signaling networks using only experimental techniques is very challenging, as performing extensive experiments is very expensive and sometimes, even impractical. Construction of signaling networks from RNAi data using computational techniques have been proposed as an alternative way to solve this challenging problem. However, the earlier approaches are either not scalable to large scale networks, or their accuracy levels are not satisfactory. In this study, we integrate RNAi data given on a target network with multiple reference signaling networks and phylogenetic trees to construct the topology of the target signaling network. In our work, the network construction is considered as finding the minimum number of edit operations on given multiple reference networks, in which their contributions are weighted by their phylogenetic distances to the target network. The edit operations on the reference networks lead to a target network that satisfies the RNAi knockdown observations. Here, we propose two new reference-based signaling network construction methods that provide optimal results and scale well to large-scale signaling networks of hundreds of components. We compare the performance of these approaches to the state-of-the-art reference-based network construction method SiNeC on synthetic, semi-synthetic, and real datasets. Our analyses show that the proposed methods outperform SiNeC method in terms of accuracy. Furthermore, we show that our methods function well even if evolutionarily distant reference networks are used. Application of our methods to the Apoptosis and Wnt signaling pathways recovers the known protein-protein interactions and suggests additional relevant interactions that can be tested experimentally.