Transcriptional programming involves inducing stem cells to be induced into a variety of cell types through the forced expression of transcription factors. In order to be truly effective, however, it is necessary to understand not just the appropriate transcription factors to use, but also potential mechanisms by which they bind to DNA and effect downstream expression. This project seeks to improve on an existing binding motif discovery method to allow for the presence of wildcard characters, or gaps in the binding sequence and then benchmark it against similar methods. Gaining a more nuanced understanding of binding motifs can lead to broader, more efficient applications for transcriptional programming.

Computational methods applied to biological problems have always interested me. I’ve had prior experience working with biological data in MIT’s BioMicroCenter, as well as during a summer fellowship at UCLA sponsored by the Huntington’s Disease Society of America.