Published July 7, 2023 | Version 1.0.0
Dataset Open

Deep model predictive control of gene expression in thousands of single cells

  • 1. Boston University

Description

Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures.

See our preprint here: biorxiv.org/content/10.1101/2022.10.28.514305

And our code repository here: gitlab.com/dunloplab/deepcellcontrol

Contents:

  • datasets.zip: Formatted experimental data used to train and validate fluorescence forecasting models.
  • experiments.zip: Processed data for all control experiments.
  • misc.zip: Files necessary to reproduce some figures or to exactly reproduce some of our results.
  • models.zip: Trained neural network models and associated files.

Files

datasets.zip

Files (2.4 GB)

Name Size Download all
md5:e43440ebb55f3b23664e1074d6b386ac
357.6 MB Preview Download
md5:3c1b0a32ddaf42f3f826cad86fe6d5e1
1.4 GB Preview Download
md5:42d46e3f2f931a1c01b9b2c2ff0415e2
473.6 MB Preview Download
md5:8761ec36e93c4eb306742c25ca848863
110.7 MB Preview Download

Additional details

Funding

National Institutes of Health
Feedback and Noise in a Multiple Antibiotic Resistance Circuit 1R01AI102922-01A1
U.S. National Science Foundation
Graduate Research Fellowship (GRFP) 1840990
U.S. National Science Foundation
Single-cell feedback, optogenetics, and deep learning to control gene expression in bacteria 2032357