Data Science Research to Support Stem Cell Therapy for Muscular Dystrophy
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This thesis project focused on using a sequence-based, high-performance computational tool to design synthetic proteins and is part of current collaborative research on Duchenne Muscular Dystrophy (DMD). A possible treatment for DMD consists of injecting patients with healthy muscle satellite cells grown in tissue culture. However, such cells cannot currently be produced in quantity because they convert to muscle cells (differentiate) prematurely. Using InSiPS, theIn-SilicoProteinSynthesizer, protein sequences were designed to interact with target proteins and inhibit the protein-protein interaction proposed to regulate the premature differentiation. The resulting sequences were predicted to interact with the target proteins with high specificity (99.98%). Complementary biochemistry experiments indicated interactions with the intended target for two out of ten synthetic proteins. These results are being studied as part of the ongoing research seeking to develop a treatment for DMD.
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Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2018
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hernandezsalas-datascienceresearchtosupportstemcelltherapy.pdf | 2023-05-05 | Public | Download |