Automated inference of production rules for glycans

TL;DR

This paper introduces an SMT-solver-based iterative method to automatically infer glycan assembly rules from cell surface glycan data, advancing understanding of glycan biosynthesis.

Contribution

It presents the first automated method using SMT solvers to infer glycan assembly rules from biological data, enabling new insights into glycan biosynthesis.

Findings

Successfully inferred glycan assembly rules from published data

Developed a new tool for automated glycan rule inference

Demonstrated applicability to real biological datasets

Abstract

Glycans are tree-like polymers made up of sugar monomer building blocks. They are found on the surface of all living cells, and distinct glycan trees act as identity markers for distinct cell types. Proteins called GTase enzymes assemble glycans via the successive addition of monomer building blocks. The rules by which the enzymes operate are not fully understood. In this paper, we present the first SMT-solver-based iterative method that infers the assembly process of the glycans by analyzing the set of glycans from a cell. We have built a tool based on the method and applied it to infer rules based on published glycan data.