Project 1: Integration of machine learning techniques with genome scale metabolic models for improved prediction.
The objective is to implement machine learning techniques for estimation of enzyme properties, which could be furthered used in the genome scale metabolic modes as additional constraints to improve the model prediction.
Read more at http://sysbio.se/
My research interest is in the computer aided understanding and design of biological systems. I had two projects during my master training period. In my first project, a diffusion-reaction model and a kinetics model were developed to study the mechanisms of the proximity channelling. Our models provide a theoretical basis for the engineering of proximity channelling between sequentially acting enzymes. In my second project, a machine learning assisted YeastFab (MLAF) method was developed for the yeast pathway balancing. Our results showed the great power of the machine learning based strategy in the field of metabolic engineering.