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  <title><![CDATA[MS Defense by Andre Berry]]></title>
  <body><![CDATA[<p><strong>Andre Berry</strong></p><p><strong>BioE M.S. Defense Presentation</strong></p><p><strong>Date:</strong>&nbsp;Thursday, May 15, 2025</p><p><strong>Time:</strong>&nbsp;2:00 PM - 4:00 PM</p><p><strong>Location:</strong>&nbsp;Ford ES&amp;T 1387</p><p>&nbsp;</p><p><strong>Advisor:</strong>&nbsp;Dr. Corey Wilson, Georgia Institute of Technology, School of Chemical and Biomolecular Engineering</p><p>&nbsp;</p><p><strong>Committee Members:</strong>&nbsp;</p><p>Dr. Raquel Lieberman, Georgia Institute of Technology, School of Chemistry &amp; Biochemistry</p><p>Dr. Matthew Realff&nbsp;, Georgia Institute of Technology, School of Chemical and Biomolecular Engineering</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Synthetic Transcription Factor Allostery Mapping and Analysis</strong></p><p>This work aims to advance the synthetic design of allosterically regulated systems by extracting sequence-function correlations from engineered LacI-based anti-repressors. Through deep mutational scanning, we have generated comprehensive datasets correlating single-mutant genotypes with functional phenotypes. Building upon the experimental dataset and alongside model development efforts that utilize it, I introduce a novel approach to complement these analyses. By projecting deep mutational scanning data onto a representative protein structure model, I enable visual inspection and procedural analysis of position-based relationships. This projection, combined with quantitative data analysis across multiple datasets, generates a comprehensive, site-specific value list that can be algorithmically manipulated. This integrated approach, blending structural visualization with quantitative analysis, provides a deeper understanding towards position linked factors integral in LacI allostery. Ultimately, this research seeks to establish a foundation for improved engineering strategies for synthetic transcription factors and to enhance the development of features relevant for phenotype prediction efforts by further elucidating the mechanistic underpinnings of anti-repressor function and contributing to the broader understanding of protein allostery.</p><p>&nbsp;</p>]]></body>
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