Project

Implementing a kinetic model for yeast glycolysis that distinguishes labeled from non-labeled metabolites

Publisher

Supervisor

Location

Greater Copenhagen area

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.

Objectives
  • Expand an existing kinetic model for yeast glycolysis to model both labeled and unlabeled metabolites.

  • Use the resulting model to explain the measured data.


Prerequisites

  • The candidate should be somewhat familiar with metabolic models: stoichiometric/constraint-based/genome-scale models.

  • 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.

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Contact

Company / Organization

DTU Biosustain

Name

Marta Matos

Position

Postdoc

Mail

mrama@biosustain.dtu.dk

Supervisor info

MSc Eng in Applied Chemistry

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc Eng in Quantitative Biology and Disease Modelling

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc in Bioinformatics and Systems Biology

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc in Biomedical Engineering

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc in Biotechnology

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc in Chemical and Biochemical Engineering

Supervisor

Marta Matos

Type

MSc thesis, Special course

MSc in Mathematical Modelling and Computation

Supervisor

Marta Matos

Type

MSc thesis, Special course

Technical University of Denmark

For almost two centuries DTU, Technical University of Denmark, has been dedicated to fulfilling the vision of H.C. Ørsted – the father of electromagnetism – who founded the university in 1829 to develop and create value using the natural sciences and the technical sciences to benefit society.


Today, DTU is ranked as one of the foremost technical universities in Europe, continues to set new records in the number of publications, and persistently increases and develops our partnerships with industry, and assignments accomplished by DTU’s public sector consultancy.

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