Plants respond to environmental stresses in many complex ways, including turning on (expressed) or off certain genes (ie. gene regulation). In particular, I am interested in how wounding stress is regulated and how this changes over time.
This project involves discovering the cis-regulatory code of plant response to wounding stress using multi-dimensional data integration and machine learning. Short sequences in gene promoter regions that are common between co-expressed genes under wounding stress are identified. Then I use information about those sequences, like their overlap with known TF binding, and their chromatin accessibility to assess how likely they are to be true regulatory elements. Finally, I build predictive models using machine learning to determine how well these putative regulatory elements can be used to predict a gene’s response to the stress conditions.