Network‐based approaches for understanding gene regulation and function in plants. (28th August 2020)

• Record Type: Journal Article
• Authors: Ko, Dae Kwan; Brandizzi, Federica
• Is Part Of: Plant journal. Volume 104:Number 2(2020)
• Page Start: 302
• Page End: 317
• Publication Date: 2020-08-28
• Subjects: gene‐regulatory networks -- transcriptional regulation -- machine learning -- plants; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.14940
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: 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. Significance Statement: Understanding gene function is a critical requirement to advance knowledge of the principles underpinning fundamental and applied plant biology. In this review, we describe predictive analyses, their strengths and pitfalls that are fast‐forwarding our understanding of gene regulation and function in plants.