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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 opensource 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.

Continuous directed evolution rapidly implements cycles of mutagenesis, selection, and replication to accelerate protein engineering. However, individual experiments are typically cumbersome, reagent-intensive, and require manual readjustment, limiting the number of evolutionary trajectories that can be explored. We report the design and validation of Phage-and-Robotics-Assisted Near-Continuous Evolution (PRANCE), an automation platform for the continuous directed evolution of biomolecules that enables real-time activitydependent reporter and absorbance monitoring of up to 96 parallel evolution experiments. We use this platform to characterize the evolutionary stochasticity of T7 RNA polymerase evolution, conserve precious reagents with miniaturized evolution volumes during evolution of aminoacyl-tRNA synthetases, and perform a massively parallel evolution of diverse candidate quadruplet tRNAs. Finally, we implement a feedback control system that autonomously modifies the selection strength in response to real-time fitness measurements. By addressing many of the limitations of previous methods within a single platform, PRANCE simultaneously enables multiplexed, miniaturized, and feedback-controlled continuous directed evolution. from the National Cancer Institute (F32 CA247274-01).

INTRODUCTIONLiquid handling robots have become a biotechnology staple 1,2 , allowing laborious or repetitive protocols to be executed in highthroughput. However, software narrowly designed to automate traditional hand-pipetting protocols often struggles to harness the full capabilities of robotic manipulation. Here we present Pyhamilton, an open-source Python package that eliminates these constraints, enabling experiments that could never be done by hand. We used Pyhamilton to double the speed of automated bacterial assays over current software and execute complex pipetting patterns to simulate population dynamics. Next, we incorporated feedback-control to maintain hundreds of remotely monitored bacterial cultures in log-phase growth without user intervention. Finally, we applied these capabilities to comprehensively optimize bioreactor protein production by maintaining and monitoring fluorescent protein expression of nearly 500 different continuous cultures to explore the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate Pyhamilton's empowerment of existing hardware to new applications ranging from biomanufacturing to fundamental biology. MAIN TEXTAutomation has been widely implemented in biotechnology 3 to facilitate routine tasks involved in DNA sequencing 4 , chemical synthesis 5 , drug discovery 6 , and molecular biology 7 . In principle, flexibly programmable robots could enable diverse experiments beyond the capabilities of human researchers, across a range of disciples within the sciences. Existing robotic software easily automates protocols designed for hand pipettes, but struggles to enable more specialized or sophisticated methods. As such, truly custom robot manipulation remains out of reach for most laboratories 2 , even those with well-established automation infrastructures.Bioautomation lags behind the rapidly advancing field of manufacturing, where robots are expected to be task-flexible, responsive to new situations, and interactive with humans or remote management systems when ambiguous situations or errors arise 2 . A key limitation is the lack of a comprehensive, suitably abstract, and accessible software ecosystem 8-10 . Though bioinformatics is becoming increasingly opensourced 11,12 , bioautomation has been slow to adopt key practices such as modularity, version control, and asynchronous programming.To address these issues, we developed Pyhamilton, a Python package that not only facilitates high-throughput operations within the laboratory, but also allows liquid-handling robots to execute previously unimaginable and increasingly impressive methods. With this package, users can use process scheduling, run simulations for experimental planning, implement error handling for straightforward troubleshooting, and easily integrate robots with external laboratory equipment. Design of Pyhamilton SoftwarePyhamilton enables Hamilton STAR and STARlet liquid handling robots to be programmed using standard Python. This allows for robotic method development to benefit from standa...