Literature DB >> 26854090

Toward the First Data Acquisition Standard in Synthetic Biology.

Iñaki Sainz de Murieta1,2, Matthieu Bultelle1,2, Richard I Kitney1,2.   

Abstract

This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.

Entities:  

Keywords:  biopart; characterization; data acquisition; standard; synthetic biology

Mesh:

Year:  2016        PMID: 26854090     DOI: 10.1021/acssynbio.5b00222

Source DB:  PubMed          Journal:  ACS Synth Biol        ISSN: 2161-5063            Impact factor:   5.110


  8 in total

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Journal:  Nature       Date:  2016-03-17       Impact factor: 49.962

Review 2.  Engineering biological systems using automated biofoundries.

Authors:  Ran Chao; Shekhar Mishra; Tong Si; Huimin Zhao
Journal:  Metab Eng       Date:  2017-06-07       Impact factor: 9.783

3.  Scaling up genetic circuit design for cellular computing: advances and prospects.

Authors:  Yiyu Xiang; Neil Dalchau; Baojun Wang
Journal:  Nat Comput       Date:  2018-10-05       Impact factor: 1.690

Review 4.  Build a Sustainable Vaccines Industry with Synthetic Biology.

Authors:  Richard I Kitney; Jennifer Bell; Jim Philp
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5.  The context matrix: Navigating biological complexity for advanced biodesign.

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6.  A Network Approach to Genetic Circuit Designs.

Authors:  Matthew Crowther; Anil Wipat; Ángel Goñi-Moreno
Journal:  ACS Synth Biol       Date:  2022-08-31       Impact factor: 5.249

7.  Utilising datasheets for the informed automated design and build of a synthetic metabolic pathway.

Authors:  Kealan Exley; Christopher Robert Reynolds; Lorna Suckling; Soo Mei Chee; Argyro Tsipa; Paul S Freemont; David McClymont; Richard Ian Kitney
Journal:  J Biol Eng       Date:  2019-01-18       Impact factor: 4.355

Review 8.  Beyond natural: synthetic expansions of botanical form and function.

Authors:  Nicola J Patron
Journal:  New Phytol       Date:  2020-04-23       Impact factor: 10.323

  8 in total

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