معرفی
Radhakrishnan Mahadevan is Professor and Canada Research Chair (Tier 1) in Metabolic Systems Engineering at the University of Toronto's Faculty of Applied Science and Engineering, Department of Chemical Engineering & Applied Chemistry. He serves as Acting Associate Chair for Undergraduate Curriculum Development and leads the Laboratory for Metabolic Systems Engineering and BioZone Centre for Applied Bioscience and Bioengineering.
Education:
- B.Tech., Indian Institute of Technology (Madras)
- Ph.D., University of Delaware
His research integrates machine learning with metabolic engineering to address challenges in industrial biotechnology, environmental sustainability, and biomedical applications. Current projects include AI-assisted biocatalyst engineering, bioengineering mine tailings for nickel extraction, and hybrid metabolic engineering approaches. His work spans genome-scale modeling, synthetic biology for dynamic metabolic control, and development of therapeutic probiotics for inflammatory diseases.
Recent publications reveal a strong trend toward machine learning integration in enzyme engineering and metabolic model optimization, with applications spanning biofuels, biochemicals, and personalized medicine. The research demonstrates increasing focus on multi-scale modeling from molecular interactions to bioreactor systems.
Scientific Awards:
- Canada Research Chair (Tier 1) in Metabolic Systems Engineering
Professor Mahadevan actively mentors 19 graduate students including 15 PhD candidates and 4 Master's students, with ongoing recruitment for new projects. His research is supported through the Canada Research Chair program and industry collaborations focused on bioprocess optimization and synthetic biology applications.
He directs the Laboratory for Metabolic Systems Engineering, which employs robotic platforms for automated cell engineering and maintains collaborations with industrial partners. The lab's multi-disciplinary team includes research scientists, postdoctoral fellows, and students working across computational and experimental domains.
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