(474f) Rapid Metabolic Flux and Free Energy Analysis Using Multi-Isotope Tracing and Machine Learning

Metabolic fluxes represent the rates at which organisms operate metabolic pathways and are a fundamental descriptor of cellular state. 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, we quantified fluxes in complex systems using novel multi-isotope tracing approaches to better understand fundamental metabolic design principles.

Using multiple isotope tracers, we 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 and manifests up to 20% higher glucose uptake in the first minutes of glucose upshift compared to an ED pathway deficient strain. The rapid utilization of the ED pathway endows E. coli cells with metabolic adaptability and evolutionary benefits in microbial communities during intermittent nutrient supply. To make flux quantitation more scalable and accessible, we innovated a two-stage machine learning (ML) flux analysis framework termed ML-Flux. ML-Flux was trained using data from five universal models of central carbon metabolism and 26 different ¹³C and ²H glucose and glutamine tracers to convert isotope labeling patterns into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we determined metabolic fluxes through central carbon metabolism at orders-of-magnitude faster speeds than traditional MFA. Additionally, ML-Flux computes Gibbs free energy of reaction, which informs on energy and enzyme usage efficiency. The ML-Flux framework is computationally light and deployed as a webtool, thereby democratizing access to flux and free energy analysis. 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 our understanding and control of metabolism.