Object classification in analytical chemistry via data-driven discovery of partial differential equations

TL;DR

This paper introduces a data-driven, PDE-based classification method for glycans in LC-MS/MS analysis, utilizing a Green's function approach and physical modeling to improve structure-function understanding.

Contribution

It develops a novel PDE-based analytical framework for glycan classification that is independent of m/z values, using a Green's function derived from a convection-diffusion-absorption model.

Findings

Successful classification of glycans using the PDE-based method

Validation against experimental mass spectrometry data

Method applicable to other physical and chemical processes

Abstract

Glycans are one of the most widely investigated biomolecules, due to their roles in numerous vital biological processes. This involvement makes it critical to understand their structure-function relationships. Few system-independent, LC-MS/MS (Liquid chromatography tandem mass spectrometry) based studies have been developed with this particular goal, however. When studied, the employed methods generally rely on normalized retention times as well as m/z - mass to charge ratio of an ion values. Due to these limitations, there is need for quantitative characterization methods which can be used independently of m/z values, thus utilizing only normalized retention times. As such, the primary goal of this article is to construct an LC-MS/MS based classification of the permethylated glycans derived from standard glycoproteins and human blood serum, using a Glucose Unit Index (GUI) as the reference frame in the space of compound parameters. For the reference frame we develop a closed-form analytic formula, which is obtained from the Green's function of a relevant convection-diffusion-absorption equation used to model composite material transport. The aforementioned equation is derived from an Einstein-Brownian motion paradigm, which provides a physical interpretation of the time-dependence at the point of observation for molecular transport in the experiment. The necessary coefficients are determined via a data-driven learning procedure. The methodology is presented in an abstract manner which allows for immediate application to related physical and chemical processes. Results employing the proposed classification method are validated via comparison with experimental mass spectrometer data.