Enzymatic pathways control a host of cellular processes, but the complexity of such pathways has made them difficult to predict. Elektrum combines neural architecture search, kinetic models and transfer learning to effectively discover CRISPR–Cas9 cleavage kinetics.
References
Zhang, Z. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00569-1 (2023).
Doudna, J. A. & Charpentier, E. Science 346, 1258096 (2014).
Lin, J. et al. Adv. Sci. 7, 1903562 (2020).
Listgarten, J. et al. Nat. Biomed. Eng. 2, 38–47 (2018).
Anzalone, A. V., Koblan, L. W. & Liu, D. R. Nat. Biotechnol. 38, 824–844 (2020).
Liu, F. et al. Nat. Mach. Intell. 5, 1261–1274 (2023).
Strohkendl, I., Saifuddin, F. A., Rybarski, J. R., Finkelstein, I. J. & Russell, R. Mol. Cell 71, 816–824 (2018).
Wessels, H. H. et al. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01830-8 (2023).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Wen, D.J., Theodoris, C.V. Interpretable model of CRISPR–Cas9 enzymatic reactions. Nat Comput Sci 3, 1011–1012 (2023). https://doi.org/10.1038/s43588-023-00570-8
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00570-8
- Springer Nature America, Inc.