We explore how living systems process information through gene regulation, and how these mechanisms evolve and diversify. By combining synthetic biology, high-throughput experiments, and evolutionary theory, our group builds large-scale models to reveal the principles that shape regulatory systems and to re-engineer them for new functions. Our current focus is on the evolution and engineering of bacterial communication systems.
- How did bacterial communication systems emerge and diversify? How can new systems evolve? How can we (re)engineer bacterial communication into novel functions?
- We study how bacteria use quorum sensing to coordinate collective behaviors such as biofilm formation and virulence. By combining phylogenetic-based reconstructions, high-throughput phenotyping of extant variants, and mapping of sequence-function relationships, we aim to uncover how communication systems originated, diversified, and how they may continue to evolve across species.
Using insights from natural systems, we design and build synthetic communication modules that allow cells to exchange information in new ways. These engineered systems open possibilities for programming collective behaviors in biotechnology and synthetic ecosystems.
Gene regulatory networks are constantly reshaped by the acquisition of new genes and transcription factors via horizontal gene transfer. We investigate how such events generate novel regulatory interactions, alter cellular functions, and contribute to the evolutionary innovation of microbes.
We are also interested in molecular components that process information beyond bacterial communication —such as transcription factors, signaling proteins, and other regulatory elements. We are open to project ideas involving components that integrate environmental cues, metabolic states, and intercellular signals to coordinate cellular behavior. By combining high-throughput phenotyping assays with evolutionary and functional analyses, we aim to uncover sequence–function relationships that reveal how information processing networks emerge, adapt, and can be rationally engineered.