Md Abdul Alim
ABSTRACT
Signaling networks play a key role in almost all cellular functions and cell's response to extracellular stimuli. Constructing the topology of interactions that make up signaling networks is of utmost importance to understand how these networks function. Knockdown assays, such as RNA interference (RNAi) technology, provides us hints about the roles of individual genes involved in these networks. However, determining the correct topology of interactions between these genes remains to be challenging. Existing computational approaches are either not scalable to large networks, or they have low accuracies. In this study, we consider that problem of constructing signaling network topology from single gene knockdown experiments. We formulate this mathematically as a network editing problem and develop an efficient algorithm to solve this problem. The key contribution in this paper is that our formulation can integrate the knowledge available in the form of the topologies of an arbitrary number of reference networks. Our experiments on synthetic, semi-synthetic and real datasets demonstrate that the proposed method greatly outperforms the state of the art methods, which are limited to a single reference network, in terms of accuracy with negligible increase in computational cost. Furthermore, our experiments suggest that the accuracy of our method remains high even when evolutionarily distant reference networks are used. Application of our method to the Apoptosis and Wnt signaling pathways recovers a large number of known protein-protein interactions.
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Index Terms
- Multiple reference networks improve accuracy of signaling network construction
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