Numerical bias estimation for mass spectrometric mass isotopomer analysis
Tae Hoon Yang1, Christoph J Bolten, Maddalena V Coppi
1Computational Department, Genomatica Inc., 10520 Wateridge Circle, San Diego, CA 92121, USA. tyang@genomatica.com
Analytical Biochemistry
|March 12, 2009
Summary
This study introduces a numerical bias estimation method for mass spectrometric isotopomer analysis. Correcting systematic errors improves the precision and reliability of metabolic flux measurements from (13)C tracer experiments.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Systems Biology
Background:
- Mass spectrometric (MS) isotopomer analysis is vital for studying biological systems with stable isotopes.
- Metabolic flux analysis quantifies intracellular pathway fluxes using mass isotopomers from labeled substrates.
- Accurate bias estimation is critical for reliable flux quantification and measurement quality.
Purpose of the Study:
- To present a model-driven method for numerical bias estimation in MS isotopomer analysis.
- To correct for unknown systematic errors specific to each mass isotopomer peak.
- To enhance the accuracy and reliability of metabolic flux measurements.
Main Methods:
- Developed a numerical bias estimation model for MS isotopomer analysis.
- Validated the method using computational simulations and experimental measurements.
- Applied the bias correction to (13)C tracer experimental data.
Main Results:
- Bias correction significantly improved the precision of isotopomer distributions and noise determination.
- Removal of estimated background signals led to residuals consistent with normality.
- Experimental variability was reduced, and data consistency was enhanced.
Conclusions:
- The developed method provides systematic error-free data for (13)C tracer experiments.
- This approach increases the reliability of computed metabolic fluxes.
- The method is adaptable for other stable isotope analyses.
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