In this project we will combine metabolic kinetic models and experimental data to study glycolysis in Saccharomyces cerevisiae. In particular, we have time-course metabolite concentration profiles for several metabolites in S. cerevisiae’s glycolysis. This data was measured in vivo by hyperpolarized NMR spectroscopy while subjecting the cells to different perturbations. We combine the experimental data with an existing kinetic model of glycolysis in S. cerevisiae to further study the cellular response to the applied perturbations. In particular, by integrating experimental data with a kinetic model we can study how metabolite concentrations that were not measured experimentally change in response to the perturbations.
However, with hyperpolarized NMR spectroscopy we can only measure labeled metabolites, which are just a fraction of the total metabolites in the cell. For instance, when we measure pyruvate using NMR, we measure only the labeled amount of pyruvate, and not the amount of pyruvate that is not labeled. Therefore, in this project, we want to expand an existing kinetic model to account for both the measured labeled metabolites and the unmeasured unlabeled metabolites. By doing this, we expect to be able to better explain the experimental data.
The candidate should have programming experience, preferably in Python and/or Mathematica.
Being familiar with differential equations, kinetic models for metabolism, and/or enzyme kinetics is a plus.