Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge
gaps hinder their efficient use for addressing urgent societal and environmental challenges …

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 …

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

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 …

New synthetic biology capabilities hold the promise of dramatically improving our ability to
engineer biological systems. However, a fundamental hurdle in realizing this potential is our …

The Design–Build–Test–Learn (DBTL) cycle, facilitated by exponentially improving
capabilities in synthetic biology, is an increasingly adopted metabolic engineering …

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess
engineering tools. They provide a unique solution to combine higher experimental …

Highlights•DBTL for cell factory development faces involution without breakthrough.•
Machine learning can assist DBTL from genetic optimizations to fermentation controls.•The …

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …

合成生物学研究中, 海量的工程化试错实验远远超出传统的劳动密集型研究范式的能力范畴,
故建立一个可以实现生命体工程化大批量合成的合成生物学研究平台迫在眉睫 …