- This is a repository of code and literature data for our publication, Zhang et al. (2022), where we offer a systematic and generalizable pipeline for high-throughput development of microbial growth and inactivation models applicable to various types of datasets.
- Our approach involves combined use of data-driven modeling and global sensitivity analysis to identify governing equations, governing process variables and their interactions effects.
- Download the complete repository and run the runDDM4Inactivation.mlx file for a comprehensive step-by-step implementation of our approach in the form of a MATLAB® Live Script that generates all the results and figures found in the publication.
- Literature data used in this study is given in ExperimentalDataset.xlsx file.
Hyun-Seob Song (hsong5@unl.edu)