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Whole-body-level metabolic flux quantitation by machine learning

Award Number: R21AT012694
ORGANIZATION: CENTER FOR DRUG EVALUATION AND RESEARCH, NIH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/17/2024
PERIOD OF PERFORMANCE END DATE: 04/30/2026

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $188,000 )
2025	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Research and Training in Complementary and Integrative Health	000	2	5/1/2025	NON-COMPETING CONTINUATION	$188,000
														Subtotal = $188,000

Issue Date FY: 2024 ( Subtotal = $230,296 )
2024	2024	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Research and Training in Complementary and Integrative Health	000	1	7/17/2024	NEW	$230,296
														Subtotal = $230,296

Grand Total All Awards = $418,296

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Whole-body-level metabolic flux quantitation by machine learning

Award Number: R21AT012694

ORGANIZATION: CENTER FOR DRUG EVALUATION AND RESEARCH, NIH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/17/2024

PERIOD OF PERFORMANCE END DATE: 04/30/2026

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