Network-based approaches for understanding gene regulation and function in plants

Abstract

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Keywords: gene-regulatory networks; machine learning; plants; transcriptional regulation.