The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/fa0782ab-760d-4fa7-babf-09bdaab0f509

van Lent, Paul; Thomas Abeel; van der Hoek, Rianne; Schmitz, Joep; Moreno Paz, Sara et. al. (2025): Data and code underlying the publication: Machine Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: a p-Coumaric Acid Case Study. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fa0782ab-760d-4fa7-babf-09bdaab0f509.v1

Categories

• Microbiology
• Genetics
• Biological Sciences

Keywords

bioprocess DNA sequencing High-Throughput Screening machine learning metabolic engineering

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

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by Paul van Lent , Thomas Abeel, Rianne van der Hoek, Joep Schmitz , Sara Moreno Paz , Irsan Kooi, Moniek Jonkers, Priscilla Zwartjens

This repository consists of the code necessary for reproduction of results in the paper Machine Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: a p-Coumaric Acid Case Study. The data consists of :

1. NMR screening data of ~3000 engineered S. cerevisiae strains (in code_mlassisted_pca.zip, data/raw/CycleTUD/), and follow-up DBTL cycle (data/raw/CycleTUDValidation)
2. DNA sequencing data (.fasta format, in 4tu_mlassisted_pca_fasta) and coverage per contig files.
3. Processed count matrices and numerical matrices that were downstream-processed from the DNA sequencing data files using .gff biobricks files.

Code is also available on the github repository

https://github.com/AbeelLab/ml-assisted-p-coumaric-acid-optimization

History

• 2025-11-18 first online, published, posted

Publisher

4TU.ResearchData

References

• https://www.biorxiv.org/content/10.1101/2025.06.13.659482v1.abstract

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Intelligent Systems
DSM-Firmenich, Department of Science and Research
Broad Institute of MIT and Harvard, Infectious Disease and Microbiome Program