Large-Scale Signaling Network Reconstruction

Date

2012-11

Author

Hashemikhabir, Seyedsasan
Ayaz, Eyup Serdar
Kavurucu, Yusuf
Can, Tolga
Kahveci, Tamer

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Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this paper, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfies the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose two methods which provide near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed methods on synthetic and real data sets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.

Subject Keywords

Signaling network, RNAi, Network editing

URI

https://hdl.handle.net/11511/28258

Journal

IEEE/ACM Transactions on Computational Biology and Bioinformatics

DOI

https://doi.org/10.1109/tcbb.2012.128

Collections

Department of Computer Engineering, Article

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Citation Formats

S. Hashemikhabir, E. S. Ayaz, Y. Kavurucu, T. Can, and T. Kahveci, “Large-Scale Signaling Network Reconstruction,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1696–1708, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28258.