On Artificial Gene Regulatory Networks

Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs.