Dataset from our paper entitled "Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism

Dataset from our paper entitled "Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism" This repo contains a partial but representative dataset from our paper entitled "Machine Learning-based Predictions of Spatial Metabolic Profiles Demonstrate the Impact of Morphology on Astrocytic Energy Metabolism". This dataset was produced by a computational model developed by Dr. Sofia Farina and presented in detail here: Farina, S., Claus, S., Hale, J.S. et al. A cut finite element method for spatially resolved energy metabolism models in complex neuro-cell morphologies with minimal remeshing. Adv. Model. and Simul. in Eng. Sci. 8, 5 (2021). https://doi.org/10.1186/s40323-021-00191-8 Please find below some comments explaining the organization of the data. For the concentration files Each row corresponds to a realization. The concentrations are organized as follows: Each 6 sequential columns correspond to one grid point (the grid points consisting of x and y coordinates are included in the file grid.csv). The metabolite concentrations are ordered as follows: [GLC], [ATP], [ADP], [GLY], [PYR], [LAC] This means that the first 6 columns correspond to the first grid point. The first column corresponds to the [GLC] concentration, the second one the [ATP] concentration, the third one to the [ADP] concentration etc. For the reaction site files There are 40 reaction centers for each realization. Each center has a set of (x,y) coordinates for a total of 80 cordinates per realization. The data files are organized as follows: The realization column indicates the realization number. For each realization number there is a total of 80 coordinates, organized in 10 rows and 8 columns (x_hxk,y_hxk,x_pyrk,y_pyrk,x_ldh,y_ldh,x_mito,y_mito).