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Automated Robotic Liquid Handling Assembly of Modular DNA Devices
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Enabling high-throughput biology with flexible open-source automation.

Emma J Chory1,2,3, Dana W Gretton1, Erika A DeBenedictis1,4

  • 1Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.

Molecular Systems Biology
|March 25, 2021
PubMed
Summary
This summary is machine-generated.

Pyhamilton, an open-source Python platform, enables complex robotic liquid handling for high-throughput biological experiments. This software allows for real-time monitoring and adjustment of hundreds of experiments, advancing fields from biomanufacturing to fundamental biology.

Keywords:
bioautomationhigh-throughput biologyliquid-handlingroboticssystems biology

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Area of Science:

  • Biotechnology
  • Synthetic Biology
  • Microbiology

Background:

  • High-throughput experimentation is crucial for understanding complex living systems but is limited by current robotic software capabilities.
  • Advanced robotic maneuvers for parallel experiment manipulation, maintenance, and monitoring are needed.

Purpose of the Study:

  • To present Pyhamilton, an open-source Python platform designed to overcome software limitations in high-throughput experimentation.
  • To demonstrate the platform's ability to execute complex pipetting for custom experiments and enable advanced applications.

Main Methods:

  • Developed Pyhamilton, an open-source Python platform for controlling robotic liquid handling systems.
  • Integrated an automated plate reader for real-time monitoring of bacterial cultures.
  • Utilized density measurements for real-time adjustment of robotic protocols.

Main Results:

  • Successfully maintained nearly 500 bacterial cultures in log-phase growth for extended periods without user intervention.
  • Systematically optimized bioreactor protein production by monitoring fluorescent protein expression and growth rates.
  • Comprehensively sampled the carbon, nitrogen, and phosphorus fitness landscape for 100 continuous culture conditions in triplicate.

Conclusions:

  • Flexible software empowers existing hardware to enable novel types and scales of biological experiments.
  • Pyhamilton facilitates high-throughput experimentation, advancing biomanufacturing and fundamental biological research.
  • The platform supports complex pipetting patterns for custom experiments, including metapopulation dynamics simulation.