Due to the highly dynamic nature of growth, data gathered through molecular analyses and high-throughput phenotyping often require custom developed software to fully capture the underlying biology. Therefore, we apply statistical techniques and machine learning approaches to link genomic and transcriptomic data to phenotypical data, to identify enhancers that drive growth-related genes and to identify spatial and temporal gradients. 

Our ultimate goal is to uncover the regulators driving plant growth and what their ideal expression patterns are. Through a close collaboration of wet lab and dry lab, we will expand and deepen the current growth regulatory network and formulate hypotheses about its modulation, which we will test through genome editing approaches. In the end, we want to provide the building blocks that allow breeders and farmers to regulate plant growth and architecture in response to environmental needs to maximize yield.