Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data
P. V. Phaneuf, D. C. Zielinski, J. T. Yurkovich, J. Johnsen, R. Szubin, L. Yang, S. H. Kim, S. Schulz, M. Wu, C. Dalldorf, E. Ozdemir, B. O. Palsson, A. Feist
This paper presents a data-driven workflow to design microbial strains using aggregated ALE mutational data, revealing gene-centric mutations that significantly alter cellular states.
bioRxiv · Jul 19, 2021Bioengineering