{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:10:52Z","timestamp":1750219852628,"version":"3.41.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Fluidic automation, the practice of programmatically manipulating small fluids to execute laboratory protocols, has led to vastly increased productivity for biologists and chemists. Most fluidic programs, commonly referred to as protocols, are written using APIs that couple the protocol to specific hardware by referring to the physical locations on the device. This coupling makes isolation impossible, preventing portability, concurrent execution, and composition of protocols on the same device.<\/jats:p>\n          <jats:p>We propose a system for virtualizing existing fluidic protocols on top of a single runtime system without modification. Our system presents an isolated view of the device to each running protocol, allowing it to assume it has sole access to hardware. We provide a proof-of-concept implementation that can concurrently execute and compose protocols written using the popular Opentrons Python API. Concurrent execution achieves near-linear speedup over serial execution, since protocols spend much of their time waiting.<\/jats:p>","DOI":"10.1145\/3558550","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T12:11:41Z","timestamp":1661861501000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Virtualizing Existing Fluidic Programs"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8458-9438","authenticated-orcid":false,"given":"Caleb","family":"Winston","sequence":"first","affiliation":[{"name":"University of Washington, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-4218","authenticated-orcid":false,"given":"Max","family":"Willsey","sequence":"additional","affiliation":[{"name":"University of Washington, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-6217","authenticated-orcid":false,"given":"Luis","family":"Ceze","sequence":"additional","affiliation":[{"name":"University of Washington, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering4020045"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Mirela Alistar and Urs Gaudenz. 2017. OpenDrop: An integrated do-it-yourself platform for personal use of biochips. See Reference Alistar and Gaudenz [1] 45.","DOI":"10.3390\/bioengineering4020045"},{"key":"e_1_3_2_4_2","first-page":"254","volume-title":"ACM SIGARCH Computer Architecture News","author":"Amin Ahmed M.","year":"2007","unstructured":"Ahmed M. Amin, Mithuna Thottethodi, T. N. Vijaykumar, Steven Wereley, and Stephen C. Jacobson. 2007. Aquacore: A programmable architecture for microfluidics. In ACM SIGARCH Computer Architecture News, Vol. 35. ACM, 254\u2013265."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/1754-1611-4-13"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1021\/acssynbio.6b00108"},{"key":"e_1_3_2_7_2","article-title":"Flexible open-source automation for robotic bioengineering","author":"Chory Emma J.","year":"2020","unstructured":"Emma J. Chory, Dana W. Gretton, Erika A. Debenedictis, and Kevin M. Esvelt. 2020. Flexible open-source automation for robotic bioengineering. bioRxiv.","journal-title":"bioRxiv"},{"key":"e_1_3_2_8_2","first-page":"365","volume-title":"Proceedings of the International Symposium on Code Generation and Optimization","author":"Curtis Christopher","year":"2018","unstructured":"Christopher Curtis, Daniel Grissom, and Philip Brisk. 2018. A compiler for cyber-physical digital microfluidic biochips. In Proceedings of the International Symposium on Code Generation and Optimization. ACM, 365\u2013377."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1021\/acssynbio.6b00304"},{"key":"e_1_3_2_10_2","volume-title":"Proceedings of the SIMB Annual Meeting","author":"Lee Peter L.","year":"2018","unstructured":"Peter L. Lee and Benjamin N. Miles. 2018. Autoprotocol driven robotic cloud lab enables systematic machine learning approaches to designing, optimizing, and discovering novel biological synthesis pathways. In Proceedings of the SIMB Annual Meeting. 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In Proceedings of the ACM Conference on Object-Oriented Programming, Systems, Languages & Applications(OOPSLA\u201918), 128.","journal-title":"Proceedings of the ACM Conference on Object-Oriented Programming, Systems, Languages & Applications"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tibtech.2015.11.006"},{"key":"e_1_3_2_16_2","unstructured":"Synthace. 2018. Antha. Retrieved from https:\/\/synthace.com\/introducing-antha."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11047-006-9032-6"},{"key":"e_1_3_2_18_2","unstructured":"Transcriptic. 2018. Autoprotocol. 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