Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.
The study, engineering and application of biological networks require practical and efficient approaches. Current optimization efforts of these systems are often limited by wet lab labor and cost, as well as the lack of convenient, easily adoptable computational tools. Aimed at democratization and standardization, we describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets. We demonstrate our workflow for various applications, from simple to complex gene circuits and metabolic networks, including several cell-free transcription and translation systems, a LacI-based multi-level controller and a 27-variable synthetic CO2-fixation cycle (CETCH cycle). Using METIS, we could improve above systems between one and two orders of magnitude compared to their original setup with minimal experimental efforts. For the CETCH cycle, we explored the combinatorial space of ~1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system. This allows to identify so far unknown interactions and bottlenecks in complex systems, which paves the way for their hypothesis-driven improvement, which we demonstrate for the LacI multi-level controller that we were able to improve by 100-fold after having identified resource competition as limiting factor. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.
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