DOI: 10.5061/dryad.t4b8gtj7b
title: Data for: The galactokinase enzyme of yeast senses metabolic flux to stabilize GAL pathway regulation

description: Nutrient sensors allow cells to adapt their metabolisms to match nutrient
 availability by regulating metabolic pathway expression. Many such sensors
 are cytosolic receptors that measure intracellular nutrient
 concentrations. One might expect that inducing the metabolic pathway that
 degrades a nutrient would reduce intracellular nutrient levels,
 destabilizing induction. In the galactose-responsive (GAL) pathway of
 S. cerevisiae, however, we find that induction is stabilized by flux
 sensing. Previously proposed mechanisms for flux sensing postulate the
 existence of metabolites whose concentrations correlate with flux. The GAL
 pathway flux sensor uses a different principle: the galactokinase Gal1p
 both performs the first step in galactose metabolism and reports on flux
 by signaling to the GAL repressor, Gal80p. Both Gal1p catalysis and Gal1p
 signaling depend on the concentration of the Gal1p-galactose complex and
 are therefore directly correlated. Given the simplicity of this mechanism,
 flux sensing is likely to be a general feature throughout metabolic
 regulation.
description: # Data for: The galactokinase enzyme of yeast senses metabolic flux to
 stabilize GAL pathway regulation
 [https://doi.org/10.5061/dryad.t4b8gtj7b](https://doi.org/10.5061/dryad.t4b8gtj7b) ## File structure The raw data is split into datasets that are stored in individual `.7z` archives. `dataset_list.md` contains a brief description of each dataset and which figures the dataset was used for. For flow cytometry data, Flow Cytometry Standard (FCS) files are shared in the `raw_data` folders. These folders also include a `meta.csv` file that contains information about each FCS file, including the genotype of the analyzed strains and where applicable, the extracellular galactose concentrations (mM) and the extracellular doxycycline concentrations (ng/µL).  For extracellular galactose concentration measurements (dataset 2), absorbance measurements at 340 nm are included for culture medium (`absorption_experimental_data.csv`) and the standard curve used to infer galactose concentrations (`absorption_standard_data.csv`). For optical density measurements (dataset 6), absorbance measurements at 600 nm at different time points are included in the `od_data.csv` file. `meta.csv` contains information about the genotype of the analyzed strains. For microscopy data (dataset 5), images from different experiments are each located in separate archives to reduce the size of individual files. These experiments are indexed in `microscopy_experiment_index.csv`. For microscopy data, processing intermediates are also included: BFP images were segmented (using [https://github.com/alexxijielu/yeast\\_segmentation](https://github.com/alexxijielu/yeast\\_segmentation)) and segmentation masks are deposited in the `masks` folder of each microscopy experiment. Segmented cells were tracked (using `cell_tracking.ipynb`) and cell tracks are saved as `tracks.csv` in the the folder of each microscopy experiment. ## Code The `code.7z` archive contains `analysis.R` which analyzes the data and saves the manuscript figures in the `figures` folder. The scripts assume the following folder structure. ``` . ├── code │ ├── analysis.R │ ├── utility_analysis.R │ ├── utility_model.R │ └── cell_tracking.ipynb ├── datasets │ ├── dataset_1 │ ├── dataset_2 │ ├── dataset_3 │ ├── dataset_4 │ ├── dataset_5 │ │ ├── microscopy_experiment_index.csv │ │ ├── 1 │ │ ├── ... │ │ └── 16 │ ├── dataset_6 │ ├── dataset_7 │ ├── dataset_8 │ └── dataset_9 └── └── dataset_10 ``` Note that `microscopy_experiment_index.csv` in the `dataset_5` folder is used by `analysis.R` to annotate the microscopy folders.