Enabling high‐throughput biology with flexible open‐source automation
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021-03-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20209942 |
| _version_ | 1827014797495894016 |
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| author | Emma J Chory Dana W Gretton Erika A DeBenedictis Kevin M Esvelt |
| author_facet | Emma J Chory Dana W Gretton Erika A DeBenedictis Kevin M Esvelt |
| author_sort | Emma J Chory |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T17:44:12Z |
| format | Article |
| id | doaj.art-c41ab37745d3457197e8dc0015561630 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2025-02-18T14:13:49Z |
| publishDate | 2021-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-c41ab37745d3457197e8dc00155616302024-10-28T09:22:03ZengSpringer NatureMolecular Systems Biology1744-42922021-03-0117311010.15252/msb.20209942Enabling high‐throughput biology with flexible open‐source automationEmma J Chory0Dana W Gretton1Erika A DeBenedictis2Kevin M Esvelt3Media Laboratory, Massachusetts Institute of TechnologyMedia Laboratory, Massachusetts Institute of TechnologyMedia Laboratory, Massachusetts Institute of TechnologyMedia Laboratory, Massachusetts Institute of TechnologyAbstract 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.https://doi.org/10.15252/msb.20209942bioautomationhigh‐throughput biologyliquid‐handlingroboticssystems biology |
| spellingShingle | Emma J Chory Dana W Gretton Erika A DeBenedictis Kevin M Esvelt Enabling high‐throughput biology with flexible open‐source automation Molecular Systems Biology bioautomation high‐throughput biology liquid‐handling robotics systems biology |
| title | Enabling high‐throughput biology with flexible open‐source automation |
| title_full | Enabling high‐throughput biology with flexible open‐source automation |
| title_fullStr | Enabling high‐throughput biology with flexible open‐source automation |
| title_full_unstemmed | Enabling high‐throughput biology with flexible open‐source automation |
| title_short | Enabling high‐throughput biology with flexible open‐source automation |
| title_sort | enabling high throughput biology with flexible open source automation |
| topic | bioautomation high‐throughput biology liquid‐handling robotics systems biology |
| url | https://doi.org/10.15252/msb.20209942 |
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