Learning Sequential Chemical Reactions
Synthetic Biology / Modeling
Type of work flexible, Master thesis, Bachelor thesis
The goal of this project is to extend the ODE-based simulation of sequential reaction networks to incorporate noise
Industrial chemical processes are often composed of a number of stages. For each stage, chemical reactions happen until equilibrium.
While similar sequential processes are used in biology experiments, the concentrations of the substances are much lower. This requires to take noise into account. Prototypical examples for procedures where noise can have a large effect are the polymerase chain reaction (PCR) and the SELEX method for selecting aptamers with desired properties. An additional challenge is that reaction parameters (such as k in the above example) are unknown.
The goal of this project is to extend the ODE-based simulation of sequential reaction networks to incorporate noise. In a second step, this simulator will be used in combination with simulation-based inference to learn reaction parameters from noisy measurements.
For further information please look into the PDF.