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Mitchell T, Beal J, Bartley B. pySBOL3: SBOL3 for Python Programmers. ACS Synth Biol 2022; 11:2523-2526. [PMID: 35767721 DOI: 10.1021/acssynbio.2c00249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Synthetic Biology Open Language version 3 (SBOL3) provides a data model for representation of synthetic biology information across multiple scales and throughout the design-build-test-learn workflow. To support practical use of this data model, we have developed pySBOL3, a Python library that allows programmers to create and edit SBOL3 documents. Here we describe this library and key engineering decisions in its design. The resulting implementation is a compact and maintainable core that provides both a familiar, pythonic interface for manipulating SBOL3 objects as well as mechanisms for building additional extensions and representations on this base.
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Affiliation(s)
- Tom Mitchell
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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Bryce D, Goldman RP, DeHaven M, Beal J, Bartley B, Nguyen TT, Walczak N, Weston M, Zheng G, Nowak J, Lee P, Stubbs J, Gaffney N, Vaughn MW, Myers CJ, Moseley RC, Haase S, Deckard A, Cummins B, Leiby N. Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis. ACS Synth Biol 2022; 11:608-622. [PMID: 35099189 DOI: 10.1021/acssynbio.1c00305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.
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Affiliation(s)
- Daniel Bryce
- SIFT, LLC., Minneapolis, Minnesota 55401, United States
| | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Tramy T. Nguyen
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Nicholas Walczak
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Mark Weston
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - George Zheng
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - Josh Nowak
- Strateos, Inc., Menlo Park, California 94025, United States
| | - Peter Lee
- Ginkgo Bioworks, Inc., Boston, Massachusetts 02210, United States
| | - Joe Stubbs
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | - Niall Gaffney
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | | | | | | | - Steven Haase
- Duke University, Durham, North Carolina 27708, United States
| | - Anastasia Deckard
- Geometric Data Analytics, Inc., Durham, North Carolina 27701, United States
| | - Bree Cummins
- Montana State University, Bozeman, Montana 59717, United States
| | - Nick Leiby
- Two Six Technologies, Inc., Arlington, Virginia 22203, United States
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