Multifactorial Optimization of a Molecular Biology Process with LabGenius

Synthace Lab - Shama and Lily
Synthace has enabled us at LabGenius to target a new area of work in our automation efforts not possible before. It is what we call on-the-fly automation. LabGenius User

Combining 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

Summary Figure of LabGenius and Antha Case Study

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.

Video shows Synthace’s Preview page directing the users as to how to set up their automated liquid handling platform. All low-level decisions are taken care of by Synthace so the user isn’t required to determine deck layouts before conducting a physical run in the lab. This decreases risk of human error or for repetitive dry run physical testing in the lab before being able to carry out execution with samples.
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