Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks

Abstract

Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.

Figure 1.

Schematic Representation of Different Types of Networks. (A) and (B) Cofunctional networks. (A) A coexpression association where two genes are linked based on a common expression pattern. (B) A cofunctional association where two genes are linked based on coexpression and/or other shared properties. (C) A gene regulatory interaction where a TF directly binds the promoter to regulate the expression of a target gene. (D) and (E) Two different approaches for uncovering GRNs using either a TF-centered approach (D) or a target-centered approach (E). (F) Protein–protein interactions reflecting an experimentally determined physical interaction between two proteins. (G) Metabolite interaction where two metabolites are linked through a common edge that represents a biochemical reaction converting one metabolite into the other. Genes are colored in black, proteins are colored in gray, and metabolites are colored white.

Figure 2.

Network Examples for Protein–Protein and Regulatory Transcriptional Interactions. Examples of networks starting from a few lateral root initiation components with proven protein–DNA and protein–protein interactions. We selected ARF7/NPH4 (AT5G20730), ARF19 (AT1G19220), SLR (AT4G14550), LBD18 (AT2G45420), LBD33 (AT5G06080), E2FA (AT2G36010), and DPA (AT5G02470) to illustrate the complexity of the networks at various levels. A coexpression network was tested for protein–protein interactions (A) and for regulatory interactions (B). We used standard settings (0.6 to 1.0) for the Pearson’s correlation coefficient, and all the available data sets in the CORNET database (https://cornet.psb.ugent.be/) (with at least meeting the conditions in three data sets) were used.