Shaun Mahony


Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
32-G534, 32 Vassar Street, Cambridge, MA 02139
email: mahony(AT)mit.edu
phone: +1-412-818-1860


Hello, and welcome to my webpage. I'm a Postdoctoral Research Associate in David Gifford's group in the Computer Science and Artificial Intelligence Laboratory at MIT.

About me:
My undergraduate degree was taken in Electronic & Computer Engineering at the National University of Ireland, Galway. I took a Ph.D. under the supervision of Terry Smith and Aaron Golden at the National Centre for Biomedical Engineering Science (also in Galway). During the course of my PhD studies, I visited Dan Rokhsar's lab in Berkeley for 5 months (2003) and Takis Benos' lab at the University of Pittsburgh for 3 months (2004). I finished my Ph.D. thesis (entitled "Self-organizing neural networks for biological sequence analysis") in October 2005. My first postdoc was taken with Takis Benos back in Pittsburgh, where my work focused mainly on aspects of regulatory evolution. I joined David Gifford's group here in MIT for my second postdoc in September 2007.

Research Interests:
In general, I'm interested in the various mechanisms of gene regulation active in eukaryotic nuclei. I'm particularly interested in how the complex set of dynamic interactions between DNA, transcription factors, histones, and the transcriptional machinery can result in precise spatial and temporal patterns of gene expression. The question of how evolutionary perturbations of this dynamic system affects gene regulation is still an open and fascinating question. Some more specific interests of mine include:

  • DNA motif-finding.
  • Evolution of gene regulation:
    • Phylogenetic footprinting and transcription factor binding site turnover.
    • Regulatory motif evolution inferred from comparative genomics.
    • "Familial" patterns of binding preference shared amongst related transcription factors.
    • The effect of DNA and protein structure on gene regulation.
    • The effect of chromatin dynamics on gene regulation.
  • Self-organizing neural networks, especially applications in biosequence analysis.