BOTany Methods: Accessible Automation for Plant Synthetic Biology.
Qiande M, Lin A, Larson L, Voiniciuc C
Summary
7.2/10Researchers created easy-to-use, code-free laboratory automation protocols for plant genetic engineering using affordable 3D-printed robots. This makes advanced research tools accessible to students and scientists without coding experience, speeding up plant breeding and genetic modification work.
Key Findings
Developed modular BOTany Methods protocols for Opentrons OT-2 robots covering molecular biology tasks from simple primer dilution to complex Plant Modular Cloning and plasmid extraction
Protocols require only table-based inputs (no Python editing) and work for users across all experience levels from undergraduates to senior scientists
End-to-end molecular cloning pipeline improves throughput, reproducibility, and traceability while minimizing manual user intervention
Original Abstract
Most members of the synthetic biology community, particularly plant scientists, lack access to liquid handling robots to scale up experiments, enhance reproducibility, and accelerate the Design, Build, Test, Learn cycle. Biofoundries enable high-throughput data acquisition to train AI models and to develop bioproducts, but they are capital-intensive to set up and not widely distributed. Entry-level, 3D-printed robots offer more affordable alternatives, but suffer from a shortage of validated protocols that can be modified without prior coding experience. To enhance access to biological automation, we developed a collection of modular BOTany Methods using Opentrons OT-2 robots to streamline the most common methods for molecular biology research and education. Our comprehensive workflow offers automation for a variety of procedures, ranging from simple but repetitive tasks (such as primer dilution and PCR setup) to more complex operations, including Plant Modular Cloning (MoClo), bacterial transformation, and plasmid extraction. Our BOTany Methods enable users across different training levels (from undergraduate students to senior scientists) to run designer experiments using table-based inputs, without editing the custom Python scripts. This pipeline enables end-to-end molecular cloning with minimal user intervention, enhancing throughput and traceability for synthetic biology applications.