Determination of Metabolic Fluxes by Multi-Isotope Tracing and Machine Learning
Authors:
Richard C. Law1* ([email protected]), Glenn Nurwono1, Samantha O’Keeffe1, Rachel Ki1, Aliya Lakhani1, Pin-Kuang Lai2, Junyoung O. Park1 (PI)
Institutions:
1University of California–Los Angeles; 2Stevens Institute of Technology
Abstract
Metabolic fluxes are a fundamental descriptor of cellular state, representing the rates at which organisms operate metabolic pathways. Mass spectrometry and isotope tracing have been instrumental in quantifying fluxes, as metabolic pathways imprint unique isotope labeling patterns on metabolites corresponding to their fluxes. Metabolic flux analysis (MFA) is a commonly used computational framework that identifies the set of fluxes that best simulate observed isotope labeling patterns. However, quantitative flux analysis remains an expert method, and the relationships between isotopic labeling patterns and fluxes remain elusive in complex metabolic environments.
Here, researchers aimed to quantify fluxes in dynamic and complex biological systems including microbial communities. Using multiple isotope tracers, the group elucidated the evolutionary benefit of the Entner-Doudoroff (ED) pathway, which is parallel to textbook (EMP) glycolysis. Tracing from two asymmetrically labeled glucose on a minutes timescale revealed that the ED pathway flux accelerates faster than the textbook glycolysis in response to nutrient upshift.
The rapid utilization of the ED pathway endows Escherichia coli cells with rapid adaptability and evolutionary benefits in a microbial community during intermittent nutrient supply. Additionally, to make flux quantitation tools more scalable and accessible, the group innovated a two-stage machine learning (ML) framework termed ML-Flux. ML-Flux is trained using data from five universal models of central carbon metabolism and 26 different carbon-13 (13C) and dihydrogen (2H) glucose and glutamine tracers to convert isotope labeling patterns into metabolic fluxes. Using ML-Flux with multi-isotope tracing, the group determined fluxes and free energies through central carbon metabolism at orders-of- magnitude faster speeds than traditional MFA. Taken together, dynamic multi-isotope tracing identified the role of parallel pathways in balancing metabolic stability and adaptability as a key design principle. ML-assisted multi-isotope tracing is a promising step toward making flux quantitation in complex biological systems increasingly accessible and expanding understanding and control of metabolism.