Nature Communications 7: Article number: 13542 (2016); Published: 6 December 2016; Updated: 5 January 2017 The original version of this Article contained an error in the spelling of the author Tommaso Poggioli, which was incorrectly given as Tommaso Pogglioli. This has now been corrected in both thePDF and HTML versions of the Article.

Introduction: Diseases are associated with gene signatures that interact in a complex network. One of the pressing open problems of computational systems biology is to decipher the topology of these networks using high throughput expression data. Despite the demonstrated effectiveness of logic-based computational algorithms to model and analyse complex biological processes, none of these symbolic approaches can decipher the topology of networks de-novo.

Introduction: Diseases are associated with gene signatures that interact in a complex network. Current computational techniques for network topology inference are only capable of predicting approximately 60% of a given gene network (that lacks complex network structures). Furthermore, the large scale of these networks prevents the use of low-throughput methods for their validation. We hypothesise that: “Inference of gene networks should be based on consistent inference algorithms, combined with automated and highly parallelised validation techniques to form an integrative, repetitive cycle of measurement, validation and network refinement”.