Computational Biology
For creative minds who wish to impact the life sciences by integrating bioinformatics, computational biology, and machine learning/AI applications
Our students learn to
earn a Ph.D. as a leadership certificate
have a plethora of research opportunities that span neuroscience to synthetic biology, atomic structures to whole human bodies, and diagnostics to molecular mechanisms
receive training in labs that integrate computational and experimental work with access to ‘warm’ clinical data
get facilitated access to entrepreneurial opportunities
Students use GPU-compatible programming on cryogenic electron microscopy to determine the structure of macromolecular assemblies like the multienzyme Aro1
Computational Biology students collaborate with cancer cell biologists to model the relation between cell form and oncogenic signals by using and advancing methods of computer vision, computer graphics, and machine learning
Students learn to combine molecular simulation with interpretable deep learning to discover principles of macromolecular self-organization. The goal is to accurately predict cryptic allosteric pathways to design unnatural protein sequences for novel functionality
Our students test and refine models of gene regulation in bacteria with thousands of high-throughput cell growth rate measurements in an upscaled turbidostat
Students develop innovative genomic approaches to model how genes and regulators influence individual cell states in development, disease, and regenerative medicine.
Our students contribute to the deep learning sequence modeling framework, Sei, that maps the global regulatory landscape of genomic sequences and predicts the effects of regulatory mutations that cause human diseases.