We develop new machine learning techniques and algorithms to model the transcriptional regulatory networks that control gene expression programs in living cells.
Our group focuses on problems that underlie cellular function and development. Currently, we’re examining how we can computationally model chromatin modifying complexes that are associated with the genome of living yeast cells. New kinds of mechanistic computational models are necessary to capture how chromatin structure encodes cellular memory, and how the state of this memory is used to control gene expression.
In particular, we are investigating new modular graphical models that use mechanistic constraints to describe biological mechanism. We’re also looking at how to build computational models of the transcriptional regulatory networks that control the differentiation of specific cell types.
Elucidating these regulatory networks will enable us to define the regulatory processes that determine a cell's progress to its terminally differentiated state, and position us to differentiate embryonic stem (ES) cells for the treatment of debilitating human diseases.
MIT scientists discover that contemporary deep learning methods recognize images with high confidence that are nonsense, which could be concerning for medical and autonomous driving decision-making
