Beyond component optimization: systems-level biodesign for lanthanide recovery

While global demand for lanthanides (Ln) is projected to rise sharply over the next decade, geographically concentrated supply chains that are sensitive to disruption create a major bottleneck for meeting future needs. Secondary feedstocks offer a potential alternative, but their low Ln concentrations and matrix complexity limit the effectiveness of conventional hydro- and pyro-metallurgical separation and enrichment. Biological systems offer selective, low-energy alternatives to conventional Ln recovery methods. Engineered Ln-binding proteins now achieve affinities and intra-Ln selectivities that, in purified form, rival or exceed those of synthetic chelators. Yet whole-system recovery depends not only on binding performance but also on envelope permeability, transport kinetics, accumulation, release, and stability under industrial leachate conditions. Moreover, no engineered microbial chassis to date integrates recognition, intracellular trafficking, accumulation, and controlled release into an end-to-end separation pipeline. Here, we outline how chassis selection and new biodesign strategies can facilitate the move from bioleaching to a full recovery pathway. This requires integrating Artificial Intellegence / Machine Learning (AI/ML)-guided design, genome engineering tools, high-throughput phenotyping, and biophysical transport modeling within a Design-Build-Test-Learn cycle applied to Ln recognition, trafficking, accumulation, and release.