Harnessing diversity towards the reconstructing of large scale gene regulatory networks

PLoS Comput Biol. 2013;9(11):e1003361. doi: 10.1371/journal.pcbi.1003361. Epub 2013 Nov 21.


Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Databases, Genetic
  • Gene Expression / genetics*
  • Gene Expression Profiling
  • Gene Regulatory Networks / genetics*

Grant support

This work is, in part, supported by funding from the HD-Physiology Project of the Japan Society for the Promotion of Science (JSPS) to the Okinawa Institute of Science and Technology (OIST). Additional support is from a Canon Foundation Grant, the International Strategic Collaborative Research Program (BBSRC-JST) of the Japan Science and Technology Agency (JST), the Exploratory Research for Advanced Technology (ERATO) programme of JST to the Systems Biology Institute (SBI), a strategic cooperation partnership between the Luxembourg Centre for Systems Biomedicine and SBI, and from Toxicogenemics program of Ministry of Health, Labour and Welfare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.