Multifactorial Optimization of a Molecular Biology Process with LabGenius

Classical vs Machine Learning driven directed evolution protein engineering methods and the EVA platform. Top panel: Classical directed evolution techniques use an iterative approach of library diversity generation and variant screening whereby improved variants are used as template sequences for subsequent iterations and knowledge of unimproved variants is discarded. Classical directed evolution approaches tend to sample local mutation space to that of the parent, limiting the sequence space investigated. Middle panel: Machine Learning driven directed evolution approaches use the data derived from variant screening of both improved and unimproved variants to direct the next round of mutations to be sampled. Machine Learning approaches investigate broader sequence space allowing for multiple solutions to be identified. Bottom panel: the LabGenius Eva platform uses Active Learning algorithms to explore a broad sequence space and fitness landscape providing a DNA library design and applies a DOE and Active Learning multifactorial optimization to each library generation process followed by Machine Learning on collected data to inform subsequent iterations.