# Whole-body-level metabolic flux quantitation by machine learning

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $230,296

## Abstract

Project Summary
Systemic diseases such as diabetes mellitus and metabolic syndrome affect multiple organs of
the body. While the human body is naturally capable of self-healing, it faces an increasing
challenge as multiple components of the systems of the human body go awry. Metabolism is a
dynamic network of biochemical reactions that support cell proliferation and biosynthesis. On the
whole-body level, metabolic networks of individual tissues and organs are connected by the
circulatory system and interfaced with the digestive and excretory systems.
Our ability to cure systemic diseases relies on a quantitative understanding of whole-body
metabolism, which requires comprehensive measurement of its dynamic states. However,
challenges arise from the lack of our ability to quantify metabolic fluxes (i.e., rates at which
pathways are utilized) on a systems level. Metabolic fluxes are a direct readout for the dynamic
state of metabolism but intangible deduced quantities that result from the catalytic interaction
between metabolites and enzymes according to the kinetic and thermodynamic laws. Metabolic
flux analysis (MFA) framework allows quantitation of metabolic fluxes by imposing mass balances
on all isotopologues resulting from stable isotope tracing experiments. As carbons form the
molecular backbone, 13C-labeled substrates are extensively employed.
The overarching aim of this project is to facilitate the measurement of metabolic fluxes on
muti-tissue and whole-body levels by tracing multiple isotope tracers. Knowledge of
metabolic fluxes offers dual benefits of laying a solid foundation for understanding and controlling
metabolism. To effectively achieve this computationally intensive goal, our teams at UCLA and
Stevens will combine deep learning with analytical, stable isotope tracing, and simulation
techniques. Using multilayer neural networks, we will develop deep learning models that predict
metabolic fluxes from the isotope labeling patterns of metabolites. With the augmented flux
determination capability, we will impart quantitative systems-level knowledge of metabolism in
individual and across tissues in co-cultures and animals.

## Key facts

- **NIH application ID:** 10791521
- **Project number:** 1R21AT012694-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Pin-Kuang Lai
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $230,296
- **Award type:** 1
- **Project period:** 2024-07-17 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10791521

## Citation

> US National Institutes of Health, RePORTER application 10791521, Whole-body-level metabolic flux quantitation by machine learning (1R21AT012694-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10791521. Licensed CC0.

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