Synthetic Biology Automation
In this project, LBNL Computing Sciences Research supported the automation of synthetic biology research pipelines supporting the design-build-test-learn (DBTL) cycle, including ingest and analysis of liquid chromatography mass spectrometry and feedstocks-to-fuels pipelines.
Publications resulting from this project:
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown, “Learning from Learning Machines: a New Generation of AI Technology to Meet the Needs of Science,” arXiv preprint arXiv:2111.13786, 27 Nov 2021.
Chris Lawson, Jose Manuel Martí, Tijana Radivojevic, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake A. Simons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay, Deepti Tanjore, Josh Dunn, and Héctor García Martín, “Machine Learning for Metabolic Engineering: A Review,” Metabolic Engineering, available online 19 November 2020. [DOI]
Reinhard Gentz, Héctor García Martin, Edward Baidoo, and Sean Peisert, “Workflow Automation in Liquid Chromatography Mass Spectrometry,” Proceedings of the 15th IEEE International Conference on e-Science (eScience), San Diego, CA, September 2019. [DOI]