Song Cao

Background and Current Projects: pursued my BS in bioinformatics at University of California, San Diego, where I had experience in both deep learning and synthetic biology. During my first research project, I studied the application of synthetic promoters in enhancing recombinant protein expression in Chlamydomonas and quantified associated transcriptional network computationally. Later, I explored network data-driven cancer cell models and incorporated the pipeline into Cytoscape to mimic in-vivo drug response. During work in the pharmaceutical industry, I designed antibody sequences and protein binder structures by combining self-devised LLMs with algorithms such as ProtBERT, RFDesign and RFDiffusion. Now I joined Hollfelder Lab to explore protein fitness landscape during directed evolution. Through advanced predictive/ generative models and high-throughput microfluidic measurements, I hope to discover the general trend behind functional protein evolution