CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.04 - July-Aug.
A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programming
Issue No.04 - July-Aug. (2013 vol.10)
Oyku Eren Ozsoy , Inf. Inst., Middle East Tech. Univ., Ankara, Turkey
Tolga Can , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.80
Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.
Network topology, Proteins, Topology, Linear programming, Data models, Optimization,linear optimization, Signaling network topology, protein-protein interactions, RNA interference
Oyku Eren Ozsoy, Tolga Can, "A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programming", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 4, pp. 869-883, July-Aug. 2013, doi:10.1109/TCBB.2013.80