Postdoctoral Researcher Computational ODE Models for Enzymatic Reaction Networks

In this project, we will develop novel quantitative high-throughput (HT) metabolic profiling methods and workflows that will facilitate the study and understanding of the dynamics of some of the building blocks of life (namely; metabolites, enzymes and cells), their interactions, and how their functioning is modulated by their environment. These novel HT workflows can be applied for a range of fields in the life sciences such as the characterization of enzyme activity, cell-based screening, drug research and future health care.

Using available knowledge about the kinetics in combination with historical data, a network-model based on ordinary differential equations (ODEs) will be built of all relevant chemical components and their dependence on the enzymes to be tested. This model will serve several goals. First, the model will be used on-line as a basis for simultaneous on-the fly estimation of relevant kinetic constants based on all measured progress curves; possibly the model has to be reduced to gain speed in solving the ODEs. This should also entail on-the-fly assessment of the quality of the estimates of the kinetic constants thereby possibly ending the experiment once a pre-set quality has been reached. Secondly, the model will be used to simulate and test different scenarios, should the measurement be done fast and less precise or slower and more precise? How many time-points are needed to obtain a pre-set quality of the estimated kinetics constants? Thirdly, the model will be used in increasingly complex mixtures and matrices to infer its robustness. This may call for adaptations of the model, e.g., simplifications which makes the model less vulnerable to the specifics of the matrices. Fourthly, relationships between model parameters and quality performance of the enzymes in applications will be established by well-chosen experiments of increasing complexity. Finally, the model will be used to find optimal mixtures of enzymes or substrates for the fermentation studied.

What are you going to do

You are expected to:

• build computational ODE models for enzymatic reaction networks;
• report and present your work on a regular basis to our industrial and academic partners;
• work closely together with analytical chemists;
• develop optimal measurement schemes for high throughput experiments;
• actively participate in scientific discussions in our research group.