Literature DB >> 27529358

Wet Lab Accelerator: A Web-Based Application Democratizing Laboratory Automation for Synthetic Biology.

Maxwell Bates1, Aaron J Berliner1, Joe Lachoff1, Paul R Jaschke1,2, Eli S Groban1.   

Abstract

Wet Lab Accelerator (WLA) is a cloud-based tool that allows a scientist to conduct biology via robotic control without the need for any programming knowledge. A drag and drop interface provides a convenient and user-friendly method of generating biological protocols. Graphically developed protocols are turned into programmatic instruction lists required to conduct experiments at the cloud laboratory Transcriptic. Prior to the development of WLA, biologists were required to write in a programming language called "Autoprotocol" in order to work with Transcriptic. WLA relies on a new abstraction layer we call "Omniprotocol" to convert the graphical experimental description into lower level Autoprotocol language, which then directs robots at Transcriptic. While WLA has only been tested at Transcriptic, the conversion of graphically laid out experimental steps into Autoprotocol is generic, allowing extension of WLA into other cloud laboratories in the future. WLA hopes to democratize biology by bringing automation to general biologists.

Entities:  

Keywords:  automation; autoprotocol; cloud-lab; democratization; scientific reproducibility; standardization; synthetic biology

Mesh:

Year:  2016        PMID: 27529358     DOI: 10.1021/acssynbio.6b00108

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


  4 in total

1.  Indicators for the use of robotic labs in basic biomedical research: a literature analysis.

Authors:  Paul Groth; Jessica Cox
Journal:  PeerJ       Date:  2017-11-08       Impact factor: 2.984

2.  A minimum information standard for reproducing bench-scale bacterial cell growth and productivity.

Authors:  Ariel Hecht; James Filliben; Sarah A Munro; Marc Salit
Journal:  Commun Biol       Date:  2018-12-06

3.  ChemOS: An orchestration software to democratize autonomous discovery.

Authors:  Loïc M Roch; Florian Häse; Christoph Kreisbeck; Teresa Tamayo-Mendoza; Lars P E Yunker; Jason E Hein; Alán Aspuru-Guzik
Journal:  PLoS One       Date:  2020-04-16       Impact factor: 3.240

4.  Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints.

Authors:  Takeshi D Itoh; Takaaki Horinouchi; Hiroki Uchida; Koichi Takahashi; Haruka Ozaki
Journal:  SLAS Technol       Date:  2021-06-25       Impact factor: 3.047

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.