Yu (Brandon) Xia
Computational Structural & Systems Biology
Department of Bioengineering, McGill University
About Research Papers People Courses Resources Jobs


Yu Xia's photo 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 of disease.

Here is my Google Scholar profile.


Email: brandon.xia@mcgill.ca
Office: Macdonald Engineering Building, Room 389
Phone: 514-398-5026 (O), 514-246-8751 (C)
Address: Yu Xia, Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, Quebec H3A 0C3, Canada


  • 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 (2012).
  • 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 (2006).
  • 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 (2005).
  • 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).
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