Reconstruction of gene regulatory networks from short time series high throughput data: Review and new findings

Abstract

The reconstruction of gene regulatory networks (GRNs) helps to improve the understanding of underlying molecular mechanisms. Many important biological phenomena, such as genetic events involved in cancer proliferation, have been attributed to these correlated gene expressions. The identification of these interactions, some of which carry signatures to clinical relevant physiological effects, sheds light on the development of various clinical applications. For example, breast cancer metastasis can be inferred from the gene networks reconstructed from high throughput data. However, the DNA microarray data usually contain large number of genes but small number of samples, thus the incorporation of the extra dimension in time may lead to further complications in capturing the gene regulations due to the curse of dimensionality. This review focuses on introducing the signal processing community the concept of GRN reconstruction. In particular, we highlight state-of-the-art methodologies and the latest challenges in GRN reconstruction from short time course high throughput data.