Systems Biology of
Transcriptional Regulatory Networks

We study bacterial pathogens to understand how transcriptional regulatory networks evolve to integrate new genetic material, and how they enable the phenotypic adaptation needed for infections. To address this question, we leverage unique high-throughput methods to characterize regulatory networks at scale. Using these networks, we can interpret data from infections to identify and characterize molecular interactions that allow bacterial pathogens to adapt to the human body and cause infections.
Additionally, we study regulatory network evolution within and between pathogenic species to identify core regulatory interactions conserved across pathogenic isolates and reveal how changes in gene content reshape regulatory networks.

We aim to define transferable molecular rules that dictate transcription factor regulatory interactions to enable the prediction and manipulation of the networks driving gene expression responses.To that aim, we combine high-throughput assays with computational modeling to decode and predict transcription factors DNA-binding specificity. Using these tools, we aim to investigate how DNA-binding preferences evolve to bridge the gap between TF protein sequence and function.
Additionally, we aim to explore the use of such predictive tools for the design of new-to-nature transcription factors with on-demand functions.