Beyond component optimization: systems-level biodesign for lanthanide recovery

Area of Science:

• Materials Science
• Biotechnology
• Chemical Engineering

Background:

• Global demand for lanthanides (Ln) is increasing, but supply chains are vulnerable.
• Conventional separation methods struggle with low concentrations and complex matrices in secondary feedstocks.
• Biological systems offer a sustainable, low-energy alternative for Ln recovery.

Purpose of the Study:

• To outline strategies for developing engineered microbial chassis for a complete lanthanide separation pipeline.
• To address the limitations of current bio-based lanthanide recovery methods.
• To integrate advanced technologies for efficient and selective lanthanide extraction.

Main Methods:

• Utilizing Artificial Intelligence/Machine Learning (AI/ML) for guided design.
• Employing genome engineering tools for microbial chassis development.
• Implementing high-throughput phenotyping and biophysical transport modeling.
• Applying a Design-Build-Test-Learn cycle for optimization.

Main Results:

• Engineered Ln-binding proteins show high affinity and selectivity, comparable to synthetic chelators.
• The study proposes a framework for integrating recognition, transport, accumulation, and release in microbial systems.
• Identified the need for whole-system optimization beyond protein-level performance.

Conclusions:

• Biodesign strategies and chassis selection are crucial for advancing lanthanide recovery.
• Integrating AI/ML and advanced genetic tools can create efficient microbial separation pipelines.
• This approach moves beyond bioleaching towards a complete, industrially viable lanthanide recovery pathway.