Wise Counsel: AI-Driven Machine Learning for Synthetic Biology Optimization with METIS

AI as a \”wise counsel\” for synthetic biology

Machine learning has transformed all fields of biology and industry. However, it is usually limited to only a few scenarios and users. Tobias Erb and a team of Max Planck Institute for Terrestrial Microbiology researchers developed METIS, a software system for optimizing biosystems. The team of researchers demonstrates the software’s versatility and usability with various biological examples.

Machine learning is now useful in all areas of biology. It is clear that the application and improvement algorithms, which are lists of instructions used to create computational procedures, is not easy. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. There is a need to bridge the gap at the intersection of computational works and experimental work between machine learning algorithms for biological systems and their application.

A team led by Tobias Erb at the Max Planck Institute for Terrestrial Microbiology has now succeeded in democratizing Machine Learning. The team, along with collaborators from the INRAe Institute, Paris, presented their tool, METIS, in a recent publication of Nature Communications. The application has a modular and versatile architecture, which means it can be used with different lab apparatus and biological systems. It does not require any computational skills. METIS stands for Machine-learning guided Experimental Trials for Improvement of Systems. It is also named after Metis, the ancient goddess of crafts and wisdom.