UPDATE: For Winter 2015, I will teach BIEN 310: Introduction to Biomolecular Engineering. This course is open to all undergraduate students in
engineering, science, and biomedicine at McGill University. There are no prerequisites for this
course. The syllabus for this course can be found here. Please email me at email@example.com if you have any questions.
I am an Associate
Professor in the Department of
Bioengineering at McGill University. In addition, I am an adjunct faculty member in the Bioinformatics Program at Boston
University, and an
affiliated faculty member in the Center for Cancer Systems Biology (CCSB)
at Dana-Farber Cancer Institute.
I graduated from Peking University with B.S. in Chemistry (major) and Computer Science (minor). I received my Ph.D. in Chemistry from Stanford University
specializing in computational structural biology under the supervision of Prof. Michael Levitt (2013 Nobel laureate in Chemistry), and carried out postdoctoral
research in bioinformatics with Prof. Mark Gerstein at Yale University.
My research interests include computational biology and bioinformatics. My current research aims to construct genome-scale computer models of biomolecular networks
with high spatial
and temporal resolutions, and to use these genome-scale models to probe physical and design principles of biological networks, and to study the systems biology
Here is my Google Scholar profile.
Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus
protein-protein interaction networks. PLoS Pathog. 9:e1003778 (2013).
Consequences of domain insertion on sequence-structure divergence in a superfold. Proc. Natl. Acad. Sci. USA 110:E3381-7 (2013).
Quantitative residue-level structure-evolution relationships in the yeast membrane proteome. Genome Biol. Evol. 5:734-44 (2013).
Toward a three-dimensional view of protein networks between species. Front. Microbiol. 3:428
Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.
PLoS Comput. Biol. 8:e1002734 (2012).
Independent effects of protein core size and expression on residue-level
structure-evolution relationships. PLoS ONE 7:e46602 (2012).
Active clustering of biological sequences. J. Mach. Learn. Res. 13:203-25 (2012).
Structural principles within the human-virus protein-protein interaction network. Proc. Natl. Acad. Sci. USA 108:10538-43 (2011).
Protein evolution in yeast transcription factor subnetworks. Nucleic Acids Res. 38:5959-69 (2010).
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids
Res. 37:5943-58 (2009).
Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human
functional linkage network. Genome Biol. 10:R91 (2009).
Structural determinants of protein evolution are context-sensitive at the residue level. Mol. Biol. Evol. 26:2387-95 (2009).
Integrated assessment of genomic correlates of protein evolutionary rate.
PLoS Comput. Biol. 5:e1000413 (2009).
Defining the TRiC/CCT interactome links chaperonin function to stabilization of newly made proteins with complex topologies.
Nat. Struct. Mol. Biol. 15:1255-62 (2008).
The role of disorder in interaction networks: a structural analysis. Mol. Syst. Biol. 4:179 (2008).
Diverse cellular functions of the Hsp90 molecular chaperone uncovered using systems approaches. Cell 131:121-35 (2007).
Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314:1938-41
Integrated prediction of the helical membrane protein interactome in yeast. J.
Mol. Biol. 357:339-49 (2006).
Assessing the limits of genomic data integration for predicting protein networks. Genome Res. 15:945-53
Analyzing cellular biochemistry in terms of molecular networks. Annu. Rev. Biochem. 73:1051-87 (2004).
Simulating protein evolution in sequence and structure space. Curr. Opin. Struct. Biol. 14:202-7 (2004).
Funnel-like organization in sequence space determines the distributions of protein
stability and folding rate preferred by evolution. Proteins 55:107-14 (2004).
Roles of mutation and recombination in the evolution of protein
thermodynamics. Proc. Natl. Acad. Sci. USA 99:10382-7 (2002).
Extracting knowledge-based energy functions from protein structures by error rate
minimization: Comparison of methods using lattice model. J. Chem. Phys. 113:9318-30 (2000).
Ab initio construction of protein tertiary structures using a hierarchical approach. J. Mol. Biol. 300:171-85 (2000).