Research Expertise and Interest
invention of biotechnology platforms, genome mining and editing, functional genomics, ML-guided protein engineering
Patrick Hsu is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley. A pioneer in the field of CRISPR gene editing, Patrick’s work aims to accelerate scientific progress through innovation in biotechnology development, science funding, and research organizations. His research group works at the intersection of synthetic biology and genomics to invent new biotechnologies for improving human health. Patrick received A.M. and Ph.D. degrees from Harvard University and his research has been recognized by the NIH Early Independence Award, the MIT Technology Review’s Innovators Under 35, the Rainwater Prize for Innovative Early Career Scientists, and the Amgen Young Investigator Award.
Current Hsu Lab areas of interest:
Molecular Technologies: To invent new biotechnologies, we develop computational platforms to survey the natural metagenomic diversity and mine for novel enzyme mechanisms. Upon discovering evolutionary innovations from the phage-host arms race, we leverage microbial genomics, functional biochemistry, and ML-guided effector engineering to create new bioengineering tools. Recent work from the lab uncovered highly efficient systems for programmable transcriptome engineering (Cas13) and targeted genomic integration of large payloads (DNA integrases).
Human Synthetic Biology: We aim to push the boundaries of synthetic biology in human cells via genome, epigenome, transcriptome, and protein engineering. First, we think backwards from major unmet therapeutic needs and then develop platform solutions that enable new kinds of genetic manipulations. Current areas of interest include cell type-specific control of biological perturbations, turning the dial on epigenetic memory, and manipulation of RNA splicing.
Functional genomics: High-throughput genetic perturbations enable us to screen thousands of genes for their contributions to phenotypes of interest: from viral infection to neurodegenerative disease processes to transcriptional regulation. We are excited about the potential of systematic mapping of genetic, cellular, and systems interactions to drive mechanistic hypotheses, new therapeutic targets, and machine learning models.