A versatile active learning workflow for optimization of genetic and metabolic networks

Abstract

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.

A gene expression control technology for cell‐free systems and synthetic cells via targeted gene silencing and transfection

“…Cell‐free transcription‐translation (TXTL) is a versatile protein expression platform used in synthetic biology. In response to a number of applications like genetic circuits (Agrawal et al, 2019; Marshall & Noireaux, 2018), metabolic engineering (Karim et al, 2020; Pandi et al, 2021), and communication networks (Dubuc et al, 2019), there is an increasing demand for precisely controlled gene expression methods. Various technologies have been proposed to regulate protein expression and abundance in cell‐free systems, such as transcription factors (Agrawal et al, 2019), CRISPR technologies (Marshall et al, 2018; Tickman et al, 2022), and the AAA+ family of proteases for tagged protein degradation (Shi et al, 2018; Shin & Noireaux, 2010).…”

A gene expression control technology for cell-free systems and synthetic cells via targeted gene silencing and transfection

“…Cell-free transcription-translation (TXTL) is a versatile protein expression platform used in synthetic biology. In response to a number of applications like genetic circuits 1,2 , metabolic engineering 3,4 , and communication networks 5 , there is an increasing demand for precisely controlled gene expression methods. Various technologies have been proposed to regulate protein expression and abundance in cell-free systems, such as transcription factors 2 , CRISPR technologies 6,7 , and the AAA+ family of proteases for tagged protein degradation 8,9 .…”