Research article

How to identify essential genes from molecular networks?

Gabriel del Rio¹ , Dirk Koschützki^2,3 and Gerardo Coello¹

¹Department of Biochemistry and Structural Biology, Universidad Nacional Autónoma de México, Instituto de Fisiología Celular, Circuito Exterior s/n ciudad Universitaria, 04510 México D.F., México

²Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, 06466 Gatersleben, Germany

³Department of Computer and Electrical Engineering, Furtwangen University of Applied Sciences, Robert-Gerwig-Platz 1, 78120 Furtwangen, Germany

author email corresponding author email

BMC Systems Biology 2009, 3:102doi:10.1186/1752-0509-3-102

Published:	13 October 2009

Abstract

Background

The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.

Results

By simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined.

Conclusion

The method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.