Research
Our group studies the process of mRNA translation by which proteins are produced in all living cells. Our long-term goals are to dissect the molecular mechanisms and systems-level regulatory principles of translation, and to decipher their functional consequences for cellular physiology and disease. Towards these goals, we use an integrated approach combining high-throughput experiments in human cells with computational modeling.
Previous studies from our lab have uncovered roles for ribosome collisions in co-translational quality control, identified cis-regulatory motifs in protein coding regions that shape mRNA stability, and dissected non-canonical translation events during viral infection.
Our current research is focused along three key directions:
1. The Genetic Landscape of Human mRNA Translation
We are creating a comprehensive map of the genes that control how messenger RNA is translated into proteins in human cells. Using our novel high-throughput screening technology called ReLiC, we can systematically turn off thousands of genes to see how each one affects protein synthesis. This helps us uncover the complex regulatory networks that govern translation in different cell types and in response to various conditions.
2. Innate Immune Roles of Non-Canonical Translation Products
We investigate the function of thousands of small, unconventional proteins called "microproteins" that are not part of the standard set of known proteins. Our hypothesis is that these rapidly evolving microproteins play crucial roles in the body's innate immune system, providing flexible defense against pathogens like viruses. We use large-scale genetic screens to identify which microproteins are produced during antiviral responses and determine how they help cells fight infection.
3. Computational Modeling of Human RNA Metabolism
We are developing computational frameworks to create predictive models of how RNA is processed, translated, and degraded within cells. Our goal is to make this modeling process more modular and intuitive for automated model generation. This enables researchers to easily build and visualize complex biochemical processes, integrating large-scale experimental data with existing knowledge.
