Flexible open-source automation for robotic bioengineering

Abstract

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...

Control of parallelized bioreactors I: dynamic scheduling software for efficient bioprocess management in high-throughput systems

“…The LHS Server opens a backdoor to take external control of a Hamilton STARlet LHS and thus the integration of such systems into greater automation workflows. Other approaches aim towards full external control, thus transferring LHS responsibilities like tracking of lab ware positions to the user [39,40]. This approach is error prone and complex because it requires intrinsic knowledge of the LHS as it circumvents the method editor supplied by the vendor.…”

The context matrix: Navigating biological complexity for advanced biodesign

“…Wherever possible, fully defined media should be used in characterisation studies, such that the chemical context of an engineered biosystem can be controlled and optimised. A recent systematic mapping of the concentration of phosphorus, carbon and nitrogen sources in a defined media to the growth rate and protein expression of E. coli cultures revealed media formulations which give high protein expression without sacrificing growth (Chory et al, 2021). This exemplifies the importance of the environmental context in achieving the desired biosystem function.…”

“…temperatures (Boo et al, 2019;Chory et al, 2021). Similarly, in a fixed environment, the same construct can display dramatically different performance in different hosts.…”

Measuring the tolerance of the genetic code to altered codon size

“…Robotics-accelerated protocol. Plaque assays were automated as previously described 34 . Briefly, the same procedure was followed as above, except that plating of the plaque assays was done by a liquid handling robot (Hamilton Robotics) by plating 20 μL of bacterial culture and 100 μL of phage dilution with 200 μL of soft agar onto a well of a 24well plate already containing 235 μL of hard agar per well.…”