Xingcheng Lin
Bio
Dr. Lin will join the Physics Department of North Carolina State University in August
of 2023 and will also be affiliated with the Bioinformatics Cluster of the Chancellor’s
Faculty Excellence Program. He received his Ph.D. in Biological Physics from the
Center for Theoretical Biological Physics and the Physics Department at Rice
University. As a graduate student, he utilized both atomistic and coarse-grained-level
simulations to study the molecular mechanism behind the invasion of influenza
viruses. Additionally, he developed simulation-based tools for characterizing folded
protein structures and simulating intrinsically disordered proteins. Dr. Lin conducted
postdoctoral research at the Chemistry Department of Massachusetts Institute of
Technology, where he expanded his research interests to the chromatin system.
There, he used coarse-grained modeling to study the organization of chromatin and
its regulation by chromatin-regulating proteins.
Area(s) of Expertise
Our research group will employ computational modeling and simulation techniques to investigate the fundamental mechanisms of epigenetic regulation – the “dark matter” of the human genome. Leveraging an ever-increasing amount of structural and sequence data, and integrating principles of physics and chemistry, our team is dedicated to developing innovative models to explore the dynamics and functions of biomolecules critical to the organization and functions of genome and epigenome, which will pinpoint potential ways to treat diseases caused by epigenetic dysregulation. In addition, we are broadly interested in other biomolecular systems with significant applications in biomedical research and therapeutics.
Publications
- Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization , (2024)
- Explicit ion modeling predicts physicochemical interactions for chromatin organization , eLife (2024)
- Interpretable Protein-DNA Interactions Captured by Structure-based Optimization , (2024)
- RACER-m leverages structural features for sparse T cell specificity prediction , SCIENCE ADVANCES (2024)
- Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies , BIOPHYSICAL JOURNAL (2024)
- Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization , (2023)
- Explicit Ion Modeling Predicts Physicochemical Interactions for Chromatin Organization , (2023)
- Explicit ion modeling predicts physicochemical interactions for chromatin organization , ELIFE (2023)
- RACER-m Leverages Structural Features for Sparse T Cell Specificity Prediction , (2023)
- Single-molecule acceptor rise time (smART) FRET for nanoscale distance sensitivity , (2023)