Computational data mining method for isotopomer analysis in the quantitative assessment of metabolic reprogramming

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biotechnology

Background:

• Stable isotope labeling is crucial for measuring metabolic flux and understanding cellular reprogramming.
• Current methods for estimating metabolic flux ratios from labeling patterns have limitations in confirming shifts between metabolic states.

Purpose of the Study:

• To develop a robust data analysis method for the quantitative assessment of metabolic reprogramming.
• To validate the method's ability to analyze metabolic redirection using comprehensive labeling data.

Main Methods:

• Utilized the Metropolis-Hastings algorithm with an in silico metabolic model.
• Generated probability distributions of metabolic flux levels based on observed 13C-labeling patterns.
• Applied quantitative assessment using Cohen's effect size (d) for detailed analysis.

Main Results:

• The developed method successfully reanalyzed literature data, demonstrating its capability for metabolic redirection analysis.
• The approach effectively uses whole 13C-labeling pattern data for accurate flux assessment.
• Cohen's effect size provided a more detailed readout of metabolic reprogramming information.

Conclusions:

• The novel data analysis method offers a quantitative approach to assess metabolic reprogramming.
• This method enhances the reliability of interpreting metabolic flux shifts from labeling experiments.
• It holds potential for diverse applications in analyzing metabolic isotopomer data from various biological samples.