2021 Annual Meeting

(647e) A Synthetic Biology Knowledge System Accelerates Design and Learning for Synthetic Biology Researchers

Authors

Kevin Keating - Presenter, Worcester Polytechnic Institute
Eric Young, Worcester Polytechnic Institute
Brandon Sepulvado, NORC at the University of Chicago
Bridget McInnes, Virginia Commonwealth University
Chris J. Myers, University of Utah
Mai Nguyen, University of California, San Diego
J. Stephen Downie, University of Illinois Urbana-Champaign
Jacob Jett, University of Illinois Urbana-Champaign
Nicholas Rodriguez, Virginia Commonwealth University
Jeanet Mante, University of Colorado Boulder
Guarav Nakum, University of California, San Diego
Logan Terry, University of Utah
Jiawei Tang, University of California, San Diego
Udayan Joshi, University of California, San Diego
Yikai Hao, University of California, San Diego
Eric Yu, University of Utah
Xiang Lu, University of California, San Diego
Rational design of engineered organisms is an inherently knowledge-rich process. Literature review on a paper-by-paper basis is time-consuming, and information gleaned from papers is often difficult to wrangle into a format suitable for downstream design workflows. This is particularly true for sequence level data, where pointers to external databases may require several steps to locate and verify the sequence referenced in a particular paper. We have developed a Synthetic Biology Knowledge System, which uses automated and manually-curated text and data mining approaches to create a repository of knowledge entities including parts, characterization data, and interactions relevant to synthetic biology. We present several workflows that highlight how this system can accelerate the design process and enable exploration of existing designs without reading papers.