Synthetic Biology: An Artificial Intelligence's Perspective

This page is intended to keep a list of relevant references on different perspectives of applying Artificial Intelligence to the research and development of synthetic biology from a researcher's perpective. Please, feel free to contribute to this list by making a pull request.

Content

1-. Survey: An overview of applying data-driven AI to Synthetic Biology.

2-. Design: Hypothesize DNA or cellular manipulations for design goals.

3-. Build: Implement design on biology, synthesize DNA, transform into cell.

4-. Test: Check desired outcome, side effects using generated data.

5-. Learn: Use data-driven AI to optimize the cycles towards the design goals.

Survey

• Artificial Intelligence for Synthetic Biology.
  Communications of the ACM, May 2022, Vol. 65 No. 5, Pages 88-97 [Paper]
  Mohammed Eslami, Aaron Adler, Rajmonda S. Caceres, Joshua G. Dunn, Nancy Kelley-Loughnane, Vanessa A. Varaljay, Hector Garcia Martin.
• The AI Hierarchy of Needs (2017) [Link]
  Rogati, M. 
• For Big-Data Scientists, 'Janitor Work' Is Key Hurdle to Insights. (2014) [Link]
  Lohr, S.

Design

• Highly accurate protein structure prediction with AlphaFold. 
  Nature Volume 596, Pages 583–589 (2021). [Paper]
  Jumper, J., Evans, R., Pritzel, A. et al.
• ProGen: Language Modeling for Protein Generation. (2020) [Paper]
  Madani, Ali and McCann, Bryan and Naik, Nikhil and Keskar, Nitish Shirish and Anand, Namrata and Eguchi, Raphael R. and Huang, Po-Ssu and Socher, Richard.
• Incorporating biological knowledge with factor graph neural network for interpretable deep learning. (Jun 2019). [arXiv]
  Ma, T. and Zhang, A.
• Predicting multicellular function through multi-layer tissue networks. 
  Bioinformatics Volume 33, No. 14 (Jul. 2017), Pages 190–198 [Paper]
  Zitnik, M. and Leskovec, J.
• Predicting the sequence specificities of DNA and RNA-binding proteins by deep learning.
  Nature Biotechnology Volume 33, No. 8 (Aug. 2015), Pages 831–838 [Paper]
  Alipanahi, B., Delong, A., Weirauch, M., and Frey, B.

Build

• Machine learning applications in systems metabolic engineering.
  Current Opinion in Biotechnology Vol. 64 (Sep. 2019), Pages 1–9 [Paper]
  Kim, G., Kim, W., Kim, H., and Lee, S.
• Systems metabolic engineering meets machine learning: A new era for data-driven metabolic engineering. 
  Biotechnology J. Vol. 14, No. 9 (Sep. 2019) [Paper]
  Presnell, K. and Alper, H.
• Machine learning for metabolic engineering: A review. 
  Metabolic Engineering Vol. 63 (2021), Pages 34–60; [Paper]
  Lawson, C., et al.

Test

• Opportunities at the intersection of synthetic biology, machine learning, and automation. 
  ACS Synthetic Biology Volume 8, No. 7 (Jul. 2019), Pages 1474–1477 [Paper]
  Carbonell, P., Radivojevic, T., and Martín, H.
• Bioprocess automation on a Mini Pilot Plant enables fast quantitative microbial phenotyping. 
  Microbial Cell Factories Volume 14, No. 1 (Dec. 2015), Page 216 [Paper]
  Unthan, S., Radek, A., Wiechert, W., Oldiges, M., and Noack, S.
• Next-generation experimentation with self-driving laboratories. 
  Trends in Chemistry Volume 1, No. 3 (Mar 2019), Pages. 282–291. [Paper]
  Häse, F., Roch, L., and Aspuru-Guzik, A.

Learn

• The experiment data depot: A web-based software tool for biological experimental data storage, sharing, and visualization.
  ACS synthetic biology Vol 6, No. 12 (Dec 2017), Pages 2248–2259.  [Paper]
  Morrell, W., et al.