Abstract
Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand‐pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open‐source Python platform that can execute complex pipetting patterns required for custom high‐throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log‐phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real‐time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.
Highlights
Introduction involved inDNA sequencing (Meldrum, 2000), chemical synthesis (Ley et al, 2015), drug discovery (Schneider, 2018), and molecular biology (Smanski et al, 2014)
Molecular Systems Biology for robotic method development to benefit from standard software paradigms, including exception handling, version control, objectoriented programming, and other cornerstone computer science principles (Table EV1, Movie EV1)
Liquid-handling robots have traditionally automated workflows that were explicitly designed for human researchers rather than enabling new high-throughput experimental modalities
Summary
Introduction involved inDNA sequencing (Meldrum, 2000), chemical synthesis (Ley et al, 2015), drug discovery (Schneider, 2018), and molecular biology (Smanski et al, 2014). Existing software for liquid-handling robots focuses narrowly on automating protocols designed for hand pipettes, while foundry languages such as Antha and remote labs such as Emerald Cloud focus on automating workflows rather than expanding experimental limits. To enable flexible high-throughput experimentation, we developed Pyhamilton, a Python package that facilitates high-throughput operations within the laboratory, with protocols that can be shared and modified. Pyhamilton allows liquid-handling robots to execute previously unimaginable and increasingly impressive methods. With this package, users can run robot simulations to troubleshoot and plan experiments, schedule experimental processes, implement error handling for quick troubleshooting, and integrate robots with external equipment
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