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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
Network-based prediction of human tissue-specific metabolism
Tomer Shlomi1, Moran N Cabili, Markus J Herrgård
1School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il
Nature Biotechnology
|August 20, 2008
Summary
This study introduces a computational method to map human tissue-specific metabolism. It reveals post-transcriptional regulation
Area of Science:
- Computational biology
- Metabolomics
- Genomics
Background:
- Investigating mammalian metabolism in vivo is challenging due to diverse tissue functions.
- Understanding tissue-specific metabolic activity is crucial for disease research.
Purpose of the Study:
- To develop and validate a computational method for analyzing large-scale human tissue-specific metabolism.
- To identify the regulatory mechanisms underlying tissue-specific metabolic profiles.
Main Methods:
- Integrated tissue-specific gene and protein expression data with a global human metabolic network reconstruction.
- Predicted tissue-specific metabolic activity across ten human tissues using computational modeling.
- Validated predictions against large-scale tissue-specificity data mining.
Main Results:
- Successfully predicted tissue-specific metabolic activity in ten human tissues.
- Identified post-transcriptional regulation as a key factor in shaping metabolic activity.
- Demonstrated that predicted gene and metabolite exchange specificities extend beyond enzyme expression differences.
Conclusions:
- Established a computational framework for genome-wide, tissue-specific analysis of human metabolism.
- Provides a basis for studying both normal and abnormal metabolism in specific tissues.
- Highlights the importance of integrating multi-omics data for comprehensive metabolic insights.
