Toward next-generation biosurfactants: Engineering rhamnolipid production from safe chassis design to scalable...

Rhamnolipid manufacturing is transitioning from empirical cultivation to programmable, multi-scale molecular bioengineering. This review highlights the transition toward safe, non-pathogenic cell factories via genetic stabilization and precursor expansion. We elucidate how integrating structural biology with machine learning-using algorithms like UniESA and MLSmut-enables structure-guided engineering of rhamnosyltransferases (RhlA/B/C), achieving a fivefold increase in noncanonical congeners and tailored chain lengths. At the bioprocess scale, interfacial engineering strategies decouple oxygen transfer from intensive foaming; bubble-free membrane aeration delivers oxygen transfer rates up to 175 mmol L^-1 h^-1, while multi-stage froth separation loops drive biomass densities to 117.2 g L^-1 with over 90% cell recovery. Finally, we connect upstream multi-omics tuning with downstream separation cascades and quantitative techno-economic assessments to guide the scalable production of application-specific rhamnolipid portfolios, ranging from environmental technical-grades to high-purity pharmaceutical surfactants.