Several factors affect the yield potential and geographical range of crops including the circadian clock, water availability, and seasonal temperature changes. In order to sustain and increase plant productivity on marginal land in the face of both biotic and abiotic stresses, we need to more efficiently generate stress-resistant crops through marker-assisted breeding, genetic modification, and new genome-editing technologies. To leverage these strategies for producing the next generation of crops, future transcriptomic data acquisition should be pursued with an appropriate temporal design and analyzed with a network-centric approach. The following review focuses on recent developments in abiotic stress transcriptional networks in economically important crops and will highlight the utility of correlation-based network analysis and applications.
Bibliographical noteFunding Information:
We would like to thank Colleen Doherty, Henry Priest and Robert Van Buren for helpful comments. This work was supported in part by Grants from the Department of Energy ( DE-SC0012639 and DE-SC0008769 to TCM), the National Science Foundation ( IOS-1202682 to MAG, IOS-1202779 to KG, IOS-1127017 to TCM, and IOS-0923752 , IOS-1025965 and IOS-1257722 to CRM) and from the Rural Development Administration, Republic of Korea (Next-Generation BioGreen 21 Programme, Systems and Synthetic Agrobiotech Centre, no. PJ009615 to CRM).