GolgiNet: Data science to take glycomics in silico and beyond

Hiren Joshi, University of Copenhagen
Grant amount: DKK 10,000,000

The “third language of life” after genes and proteins is that of complex sugars. This language (the glycocode) describes myriad ways that organisms have fine-tuned proteins and cellular functions to allow complex life to thrive. We know how 100s of enzymes generate sugars in cells, but we do not know how the individual cell regulates its enzymes and glycosylation network to make specific sugars required in health and diseases. The goal of this project is to learn how the glycocode is regulated, and in doing so reveal its functions. Using data science, the project team will build a foundational machine learning model for in silico glycoscience: GolgiNet. Capturing the regulatory patterns of cellular glycosylation, GolgiNet will be used to predict biological functions, reveal the sugars of a single cell, and predict the sugar-coated proteins a cell is programmed to make. GolgiNet will transform our ability to understand the third language of life, providing a Rosetta stone to decipher how sugars can mediate biological interactions.

Project participants
Hiren Joshi, Associate Professor
University of Copenhagen, Department of Cellular and Molecular Medicine