“…The DBTL cycle is becoming increasingly relevant due to advancements in affordable and high-throughput gene synthesis (Kosuri et al, 2010;Rogers & Church, 2016) and the development of efficient gene assembly, such as Golden Gate cloning, and the related standardized Modular Cloning (MoClo) toolkit, that enable one pot and one-step synthesis of multi-gene constructs from standardized constituent parts (Engler et al, 2008;Crozet et al, 2018). Also, rapid advancements in mutagenic and transgenic efficiencies (Ferenczi, Pyott, Xipnitou, & Molnar, 2017;Serif et al, 2018;Angstenberger et al, 2018;Picariello et al, 2020), increasing understanding of regulatory elements (Baier, Jacobebbinghaus, Einhaus, Lauersen, & Kruse, 2020;Mehrshahi et al, 2020;Rose, 2019), coupled with high-performance screening techniques enabled by optimization of microfluidics (Nouemssi et al, 2020;Saad et al, 2019;Südfeld, Hubáček, D'Adamo, Wijffels, & Barbosa, 2020;Wheeler et al, 2003), have cumulatively enabled the automation of DBTL workflow (Carbonell et al, 2018). Most notably, the integration of machine learning has exponentially increased productivity in recent years (Carbonell, Le Feuvre, Takano, & Scrutton, 2020;Opgenorth et al, 2019;Radivojević, Costello, Workman, & Garcia Martin, 2020).…”