CHBE 565-International Paper Co Seminar-Prof. Rudiyanto Gunawan, University at Buffalo (Host: Prof. Chris Rao) "Systems Glycoinformatics – Decoding Glycosylation Through Systems Biology and Machine Learning"
- Sponsor
- Chemical & Biomolecular Engineering and International Paper Company
- Contact
- Christy Bowser
- cbowser@illinois.edu
- Phone
- 217-244-9214
- Originating Calendar
- Chemical & Biomolecular Engineering - Seminars and Events
Abstract: Glycosylation is the most common post-translational modification (PTM) in mammalian systems, whereby proteins and lipids are decorated with oligosaccharide structures called glycans. These glycans regulate virtually all aspects of cellular function, and their dysregulation is implicated in diseases including cancer and blood disorders. Yet glycosylation remains far less understood compared to other PTMs and cellular processes, owing to fundamental and technological challenges: glycans are synthesized through non-template-driven enzymatic processes and exhibit extraordinarily high structural diversity, while high-throughput glycomics profiling lags significantly behind genomics and proteomics. In this seminar, I will present collaborative projects addressing these challenges through systems biology, bioinformatics, and machine learning, collectively termed "Systems Glycoinformatics." These projects span multiple omics layers, from gene expression to the glycome, and cover topics including glycoenzyme ontology and single-cell glycogene expression analysis, deep learning for glycan structure prediction, and glycosylation flux analysis with applications to therapeutic protein bioprocessing.
Biography: Dr. Rudiyanto Gunawan is an Associate Professor in the Department of Chemical and Biological Engineering at the University at Buffalo, State University of New York. His research expertise spans computational systems biology, bioinformatics, and machine learning, with a focus on understanding complex biological systems. His laboratory develops and applies innovative computational tools that extract mechanistic and actionable insights from biological data, leveraging rigorous mathematical modeling, systems analysis, deep learning, and optimization algorithms. Current research interests include glycoinformatics, single-cell omics, and bioprocess engineering, with an emphasis on bridging data-driven and mechanistic modeling approaches. The group's findings have been disseminated in over 100 publications spanning systems biology, bioinformatics, bioprocess and metabolic engineering, and biogerontology.