Inferring stable gene regulatory networks from steady-state data

Reconstructing gene regulatory networks from gene expression data has received tremendous attention in functional genomics following advancements in microarray technology. These networks provide the framework for medical diagnosis, drug design, disease treatment and biological research. Several approaches from simple clustering to highly complex hybrid techniques have been proposed in literature to understand the regulatory roles of genes and proteins. The nature of the data from microarray experiments however poses a huge informatics challenge for accurate network identification. In this paper, we present the least absolute shrinkage and selection operator vector autoregressive (Lasso-VAR) technique that incorporates stability constraints through Geršgorin's theorem for inferring stable, sparse and causal genetic networks from steady-state data.