PI: Yoshie Hanzawa (University of Illinois at Urbana-Champaign)
Co-PIs: Olgica Milenkovic, Bruce Hajek, and Steven C. Huber (University of Illinois at Urbana-Champaign)
Senior Personnel: Peter M. Yau (University of Illinois at Urbana-Champaign)
Successful completion of this project to study the photothermal flowering gene regulatory network in soybean will provide a systematic understanding of genetic networks associated with a plant's response to environmental signals, and ultimately allow plant improvement towards enhanced adaptation to fluctuating environments while improving yield. This work will provide novel algorithmic methods and bioinformatics pipelines for network inference and enhance development of fundamental knowledge in plant biology and bioinformatics. Finally, mathematical network models obtained through this work will lay the foundation for development of in silico systems to examine and predict plant performance under various environments. This project enhances integration of two very different fields of research in Crop Sciences and Engineering, and provides outstanding training opportunities for postdoctoral researchers and graduate, undergraduate and high school students in interdisciplinary research involving molecular genetic, biochemical, systems biology, and modeling approaches. Undergraduate students will be recruited from underrepresented groups and routinely involve in lab activities. The project will also organize and hold a summer camp for local high school students who will have the opportunity to conduct a summer research project on crop genetics and improvement.
A growing concern in the world is the changes in crop performance associated with global climate change. In soybean, flowering transition is regulated by the interaction between photoperiods and temperature, known as photothermal effects. To clarify the molecular mechanisms of photothermal flowering in soybean, the project will employ interdisciplinary approaches including: 1) elucidation of global gene expression patterns underlying photothermal flowering of soybean, 2) identification of key transcription factor-target interactions and protein-protein interactions of more than 20 transcription factors in the photothermal flowering gene network, 3) reverse engineering of the photothermal flowering gene network using transcriptome and interactome data as well as other biological side information by developing algorithmic methods and pipelines for network inference, and 4) mathematical modeling of the photothermal flowering gene network to provide a deeper understanding of the molecular mechanisms accounting for the genetic interactions and their functions, and biological relevance of genetic interactions implemented in the photothermal flowering gene network. All data, biological resources and bioinformatics tools obtained in this work will be made available through a project website and through public community portals that include NCBI SRA and SoyBase.
