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A computational method using differential gene expression to predict altered metabolism of multicellular organisms
Lvxing Zhu1, Haoran Zheng, Xinying Hu
1School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China.
Molecular Biosystems
|October 4, 2017
Summary
This study introduces a new computational method to predict metabolic flux differences in organisms by integrating gene expression data with metabolic networks. The approach accurately identifies metabolic alterations in bacteria and human cancer cells without needing uptake or secretion rates.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Altered metabolism is crucial in physiology and disease, but predicting metabolic flux in multicellular organisms is challenging due to undefined objective functions.
- Existing methods like enrichment analysis and constraint-based models have limitations in capturing condition-specific metabolic network interactions.
Purpose of the Study:
- To develop and validate a novel computational method for predicting differential metabolic fluxes between two conditions.
- To apply the method to both microbial (E. coli) and multicellular (human clear cell renal cell carcinoma) systems.
Main Methods:
- Integration of gene expression data with a comprehensive human metabolic network reconstruction.
- Qualitative prediction of significantly differential metabolic fluxes without requiring prior knowledge of metabolite uptake or secretion rates.
- Simultaneous consideration of metabolic network conditions and interactions, differing from traditional approaches.
Main Results:
- Accurate prediction of altered metabolic fluxes for E. coli strains under varying chemostat dilution rates.
- Reasonable prediction of metabolic alterations in clear cell renal cell carcinoma compared to normal kidney cells.
- Mapping of differential reactions to metabolic subsystems revealed key changes in ccRCC metabolism.
Conclusions:
- The proposed computational method effectively predicts genome-wide metabolic alterations in both microorganisms and multicellular organisms.
- This approach offers a valuable tool for studying altered metabolism across diverse biological contexts and conditions.
- The method demonstrates high accuracy and provides more biologically relevant predictions compared to existing studies.
