Metabolic Engineering / Dynamic Modeling

Learning what a pathway is doing when the measurements are imperfect.

Isotope-informed dynamic modeling of plant sphingolipid metabolism, with uncertainty treated as part of the scientific problem.
Published Question → model → evidence
Published graphical abstract tracing Arabidopsis cell cultures and isotope-labeled metabolomics through turnover estimation, dynamic metabolic flux analysis, enzyme perturbations and enzyme-cost analysis.

The published graphical abstract connects isotope-labeling experiments to regularized dynamic metabolic flux analysis and enzyme-level interpretation. Displayed in full without cropping. Osinuga et al., iScience (2024) · CC BY-NC-ND 4.0.

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The Question

How do you quantify transient metabolic fluxes when the experimental data is sparse and noisy?

My Contribution

r-DMFA framework, 15N isotope-labeling, targeted metabolomics, dynamic flux sampling. Identified SBH, LCBK control nodes.

  • Isotope labeling integration
  • Dynamic flux analysis
  • Uncertainty quantification
  • Control point identification
  • Arabidopsis metabolism

What the record shows

iScience publication demonstrates flux identification under data variability.

What this does—and does not—establish

Point estimates of fluxes carry uncertainty; validated against biological priors, not independent kinetic measurements.

Resources

iScience

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Publication records

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