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Updated: Jul 1, 2025
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A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
Published on: February 4, 2017
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Quantification of metabolic activity from isotope tracing data using automated methodology
Shiyu Liu1,2, Xiaojing Liu1,3, Jason W Locasale1,2,3
1Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham NC 27710, USA.
Biorxiv : the Preprint Server for Biology
|March 11, 2024
Summary
This study introduces an AI-powered pipeline for analyzing isotope tracing data, improving metabolic activity prediction and quantifying flux uncertainty. The new method reveals reprogrammed metabolic cycles in cancer cells, offering new insights into metabolic network activity.
Area of Science:
- Metabolic Engineering
- Computational Biology
- Systems Biology
Background:
- Isotope tracing is crucial for studying cellular metabolism but data interpretation is challenging.
- Current artificial intelligence (AI) applications are limited by a lack of evaluative knowledge in metabolic networks.
Approach:
- Developed a novel computational pipeline for efficient metabolic activity prediction in large metabolic networks.
- Quantified flux uncertainty inherent in traditional computational methods.
- Created an algorithm to significantly reduce uncertainty, enabling robust metabolic evaluation with limited data.
Key Points:
- Discovered highly reprogrammed mitochondria-cytosol exchange cycles in human tumor tissues.
- Identified similar metabolic patterns in cancer cells, influenced by nutritional conditions.
- Demonstrated robust automated quantification of metabolism using limited data.
Conclusions:
- The refined methodology offers a powerful tool for automated metabolic quantification.
- Provides new insights into metabolic network activity, particularly in cancer.
- Advances the application of AI in interpreting complex biological data.
