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“Engineering biomolecular regulation via high throughput assays and deep learning”

Abstract: Engineering biological sequences with desired functions is a major goal of Synthetic Biology. This is generally a challenging problem, however, due to our incomplete knowledge of biological mechanisms and an astronomically large number of possible sequences. In this talk, I will describe a biological engineering paradigm that combines high throughput experimental assays and deep learning methods. I will illustrate the potential of this approach by focusing on two design problems. In our first problem, we designed mRNA regulatory sequences to optimize protein translation, and demonstrated their performance in an mRNA-delivered gene editing therapy. In the second ongoing application, we are engineering sequences that restrict gene expression to specific human cell types.

Biography: Dr. Sebastian Castillo-Hair is a Data Science Postdoctoral Fellow at the eScience Institute and the Department of Electrical and Computer Engineering at the University of Washington. Previously, he obtained his PhD in Bioengineering at Rice University. He conducts research in Synthetic Biology, with a focus on combining high-throughput experimental assays and machine learning to engineer novel biomolecular systems.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

The 2022-2023 seminars will be virtual, and are free and open to the public.