Multifactorial Optimization of a Molecular Biology Process with LabGenius
Read the case study in fullCombining active learning with Synthace’s multifactorial optimization
Synthace enabled LabGenius to physically execute multifactorial experimental designs in an automated and autonomous manner to optimize their ssDNA extension process.

Key findings
LabGenius combined Synthace with their proprietary EVA platform and achieved:
70%
Time saving
Synthace improved efficiency, generating up to a 70% time saving on experimental design and execution
30%
Consumable saving
Optimization of process reduced consumable use by a third
15
Hours saving per iteration
Synthace provides up to 15 hours time saving per DNA library optimization

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.

Simple, rapid and flexible workflow prototyping in Synthace’s Workflow Builder. The graphical user interface of Synthace’s Workflow Builder affords a biological scientist the ability to rapidly prototype automated liquid handling workflows through programming at a higher level of abstraction with respect to most hardware vendors automated liquid handling software. The DOE workflow used for driving the execution of both DOE iterations in this study is shown here.