Hanson Lee on Muck Rack

Hanson Lee

Bellevue
As seen in: MDPI, Ellensburg Daily Record, Bellevue Reporter, Kirkland Reporter, Mercer Island Reporter, Redmond Reporter, Bothell/Kenmore Reporter, Snoqualmie Valley Record, Issaquah-Sammamish Reporter, CWU Observer
Digital Journalism major | Storytelling with a passion for running
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Articles

Extrusion-Based Printing of Myoblast-Loaded Fibrin Microthreads to Induce Myogenesis

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By Hanson Lee, Bryanna L. Samolyk, George D. Pins
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

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By Neil Thomas, David Belanger, Chenling Xu, Hanson Lee
| biorxiv.org
Abstract Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds.
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Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

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By Neil Thomas, David Belanger, Chenling Xu, Hanson Lee
| biorxiv.org
Abstract Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology with broad applications. In this work, we develop a technique for designing active and diverse libraries of protein variants by blending evolutionary information and experimental data from an ultra-high-throughput functional screen using machine learning (ML).
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