합성생물학연구실

    Genome sequencing information for individuals are now accumulating faster than ever, and predictive biomarkers for diagnosing cancers and human diseases become more accurate and reliable due to better understanding on the molecular mechanism. Recently, RNA-switch-based molecular diagnostics on a regular paper for rapid and low-cost RNA detection of Ebola and Zika viruses has been demonstrated. Clinically relevant concentration of viral samples can be detected by programmable RNA-based sensors or RNA-guided CRISPR systems. We are exploring such core molecular mechanisms that can target a diverse range of biomolecules in a more accurate and sensitive way. Our lab leverages artificial intelligence (AI) to design biological systems with greater precision and efficiency. While traditional synthetic biology has relied heavily on experiment-driven approaches to construct and optimize genetic circuits, we employ deep learning algorithms to learn the regulatory principles of gene expression and use this knowledge to generate and predict the function of novel biological parts. For example, we develop AI models that can generate entirely new promoter sequences for specific organisms or quantitatively predict their transcriptional strength. In particular, generative models trained on biological datasets enable us to design functional sequences that would be difficult to discover through conventional mutagenesis. These sequences can be evaluated in silico using CNN-based predictive models and rapidly validated in vitro using cell-free transcription assays. We are also conducting research on minimizing the number of genetic parts within gene circuits to improve strain viability and enhance the predictability of circuit behavior. By streamlining genetic circuits, more complex sign