Metabolic engineering is an important field in biotechnology, aimed at optimizing cellular processes to produce desired compounds. In this thesis, we focus on predicting the metabolome from the proteome, as understanding this relationship is crucial for understanding cellular metabolism. We investigate the usage of additional biological information like protein-protein interactions and cellular stoichiometry to improve the predictive performance of metabolome prediction models. We also employ explanation algorithms to gain key insights into the regulatory processes of a yeast cell.

We demonstrate the effectiveness of our approach by predicting the metabolic fold-change of multiple yeast kinase knockouts. Our results show that incorporating additional biological information does not significantly improve the accuracy of the metabolome prediction models. Furthermore, we identified enzymes that are relevant for all metabolites used in this study, which indicates the existence of a global set of regulatory enzymes.

Overall, our study shows that through careful manipulation of the limit amount of data decent performance can be expected when predicting the metabolome. We apply a broad spectrum of machine learning algorithms to identify optimal model architecture. The methods and insights presented in this thesis could be used for creating a general pipeline for predicting a broad spectrum of metabolites from the proteome.

Bluetick offers a juridical research platform that enables lawyers to search for cases and jurisprudence efficiently. Most Dutch legal alternatives are still old-fashioned search engines. Bluetick wants to move towards a zero-search-based approach where the system learns about the user's preference and provides them with recommendations. For the user, this means that they have to spend less time searching for cases while still finding all the relevant material. To reach this goal of zero-search, the quality of the recommendations must be high. Therefore improvements in this area are believed to result in a more lucrative product.

This report describes the process of improving the version of the recommender system that was already implemented by Bluetick. The main contributions are evaluated by their effect on the recommender system, and their role in creating a more maintainable, extensible and transparent product.

The first contribution of the team was a refactor of the old system. Using classes and interfaces, the new version makes it easier to do advanced computations on the results, while the interface makes it easier for Bluetick to add additional parts on which recommendations can be based. Secondly, similar to many existing webshops, the new system provides the user with insight into why items are recommended. Lastly, the user is now able to provide the system with relevant law articles at the start, so that the recommender system can give recommendations before the first search.