Rana Barghout is a PhD candidate in Chemical Engineering at the University of Toronto specializing in machine learning applications for enzyme function prediction and metabolic engineering. With eight years of research and hands-on lab experience across UBC and industry, she bridges experimental bioprocessing—running bioreactors and designing extraction and cell-culture assays—with computational metabolic modeling. Her work in the Mahadevan Lab and Chemical Cognition Lab focuses on translating ML-driven insights into practical strain and pathway design, evidencing a rare blend of wet-lab discipline and algorithmic rigor. She has led cross-disciplinary projects from prototype hardware to biochemical assays, showing both leadership and pragmatic engineering skills. Based in Toronto, she brings an applied mindset to complex biological systems, often uncovering non-obvious links between experimental constraints and model assumptions.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Applied Science - BASc, Chemical Engineering, Bachelor of Applied Science - BASc, Chemical Engineering at The University of British Columbia
Doctor of Philosophy - PhD, Chemical Engineering, Doctor of Philosophy - PhD, Chemical Engineering at University of Toronto
Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic models (ecGEMs) reconstruction
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Rana Barghout - Doctoral Student at University of Toronto