Dr. Huan Chen

Transcription factors (TFs) are trans-regulatory elements. They bind to cis-regulatory elements (CREs) (i.e., DNA sequences that regulate nearby genes). Our recent advances in AI and synthetic biology have enabled sequence-based modelling of key aspects of plant gene regulation, namely TF binding, CRE activity, and gene expression. However, these models still lack environmental and developmental context and thus often miss the activity of gene regulatory networks with environmental or developmental inputs that are key for expression of phenotypic traits. Therefore, I aim to develop an environment/development/tissue/cell-type-aware model enabling identification and engineering of causal links between regulatory sequences and gene expression in dynamic environments with a specific focus on barely. To achieve this, I will closely couple machine learning with high-throughput screening and synthetic biology approaches in a Design–Build–Test–Learn (DBTL) loop.