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. 2016 Aug 1;10 Suppl 2(Suppl 2):53.
doi: 10.1186/s12918-016-0301-4.

Identifying the topology of signaling networks from partial RNAi data

Yuanfang Ren  1 , Qiyao Wang  2 , Md Mahmudul Hasan  2 , Ahmet Ay  3 , Tamer Kahveci  2
Affiliations

Identifying the topology of signaling networks from partial RNAi data

Yuanfang Ren et al. BMC Syst Biol. .

Abstract

Background: Methods for inferring signaling networks using single gene knockdown RNAi experiments and reference networks have been proposed in recent years. These methods assume that RNAi information is available for all the genes in the signal transduction pathway, i.e., complete. This assumption does not always hold up since RNAi experiments are often incomplete and information for some genes is missing.

Results: In this article, we develop two methods to construct signaling networks from incomplete RNAi data with the help of a reference network. These methods infer the RNAi constraints for the missing genes such that the inferred network is closest to the reference network. We perform extensive experiments with both real and synthetic networks and demonstrate that these methods produce accurate results efficiently.

Conclusions: Application of our methods to Wnt signal transduction pathway has shown that our methods can be used to construct highly accurate signaling networks from experimental data in less than 100 ms. The two methods that produce accurate results efficiently show great promise of constructing real signaling networks.

Keywords: Missing data; Network inference; RNAi data; Signal transduction networks.

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Figures

Fig. 1
Fig. 1
An hypothetical signaling network. Nodes v s and v t are the receptor and reporter genes. Nodes v a and v b are constrained to be critical genes
Fig. 2
Fig. 2
Effect of parameters on the inference methods. a, b, and c show the average distance between the constructed and the reference networks for varying network size, noise and number of unknown genes respectively. d, e, and f show the running time of the inference methods for the same setup. The running time is reported in milliseconds (ms) and presented in log-scale
Fig. 3
Fig. 3
Comparison of the Sloan and TopSoG ranking strategies. a shows the distance between the inferred and the reference networks. b reports the running time of the inference algorithm when employed with each strategy in milliseconds (ms)
Fig. 4
Fig. 4
Comparison of the prioritized and the exhaustive methods. a shows the average distance between the inferred and reference networks. b reports the running time in milliseconds (ms)
Fig. 5
Fig. 5
The F-score of the constructed Wnt signaling network using different reference networks. a shows the F-score for target network xla. b shows the F-score for target network mmu
See this image and copyright information in PMC

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