Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Concerns over climate change have necessitated a rethinking of our transportation
infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced …

Through advanced mechanistic modeling and the generation of large high-quality datasets,
machine learning is becoming an integral part of understanding and engineering living …

Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules
such as renewable biofuels or anticancer drugs. However, traditional synthetic biology …

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection,
adaptive evolution, and metabolic flux control. However, designing TFBs with superior …

Metabolic engineering encompasses several widely-used strategies, which currently hold a
high seat in the field of biotechnology when its potential is manifesting through a plethora of …

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 …

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking
technologies, developed over the past 20 years, have enormously accelerated the …