iCH360 manuscript: code and data This repository contains code and data required to reproduce all results in: A compact model of Escherichia Coli core and biosynthetic metabolismavailable athttps://arxiv.org/abs/2406.16596 Note: This Zenodo record is a copy of the following github repository: https://github.com/marco-corrao/iCH360_paper/tree/d14ed332ed9d4fee2cc3b092cb36e848f3a63c52but additionally contains some heavy files (namely, the enumerated EFMs for each condition and a local EQuilibrator cache used for thermodynamic constant estimation) that were above the github file size limit. Using iCH360 This repository is only intended to provide the files and tools to reproduce all results in the paper. If you wish to use iCH360 for your own work, please go to the model repo: https://github.com/marco-corrao/iCH360 where you'll find the most up-to-date version of the model and its variants. Navigating the repository ./ModelContains the metabolic models (in `JSON` and `SBML` formats) mentioned in the paper, namely:- The main stoichiometric model, *i*CH360- The enzyme-constrained model variant, EC-*i*CH360- The reduced model variant *i*CH360red./VisualisationContains all the relevant metabolic maps, ready to be loaded in Escher for visualisation [1]:- The full model map- The compressed model map- The maps for each metabolic subsystem- The maps for the pathways not included in the model, but used to compute the equivalent biomass reaction used in the model../AnnotationContains annotation maps to the EcoCyc database [2]../Knowledge_graphContains the computational pipeline used to build the knowledge graph complementing the stoichiometric model, as well as the final graph structure in GML (.gml) and cytoscape (.cyjs) formats./AnalysisContains the Python scripts required to reproduce all analyses mentioned in the paper. More specific details are provided in each subfolder./EFMContains the pipeline for creating the reduced model variant *i*CH360red, as well as counting and enumerating its elementary flux modes (EFMs)../Enzyme_ConstraintsContains the data and scripts used to construct the enzyme constrained model EC-*i*CH360 and fit its turnover numbers to measured enzyme abundances../ThermodynamicsContains the file and script required to compute the estimates of thermodynamic constants for the reactions and metabolites in the model./Experimental_dataContains experimental data (proteomics, metabolomics, and fluxomics) from other works, mapped to the model../External_database_dataContains mappings between genes and polypeptides retrieved from the EcoCyc database [2]../Manuscript_FiguresContains all the notebooks (in Python and R) required to reproduce the figures in the paper. Dependencies The following packages are used throughout the repo:```# General dependencies (used throughout)cobra==0.29.0numpy==1.24.0scipy==1.10.1pandas==1.5.3matplotlib==3.7.1seaborn==0.12.2networkx==3.0tqdm==4.65.0requests==2.28.2 Additional dependencies are required to reproduce some analyses: # EFM enumerationefmtool==0.2.1 # turnover number fitting procedure:gurobipy==11.0.1 #requires a valid GUROBI licencecasadi==3.6.3 # MDF analysisgurobipy==11.0.1 #requires a valid GUROBI licence # Thermodynamic constant estimationequilibrator-api==0.4.7equilibrator-assets==0.4.1cvxpy==1.5.2``` **Notes** The following steps may be needed to correctly run enkie and eQuilibrator to reproduce the thermodynamic analysis performed on iCH360. 1. For first time use of enkie, used in (./Analysis/PTA/pta.ipynb), it may be necessary to create the folder ```~/.cache/enkie``` in your home directory(see https://gitlab.com/csb.ethz/enkie/-/issues/1) 2. If issues are encountered running eQuilibrator in (./Thermodynamics/free_energy_estimation/drg0_estimation.ipynb), it may be necessary to manually save the files from the following repos: - https://zenodo.org/records/4128543 - https://zenodo.org/records/4013789 - https://zenodo.org/records/4010930 to ```~/.cache/equilibrator``` We kindly thank Benjamin Luke Coltman for suggesting these fixes.References 1. King, Z. A. et al. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways. PLOS Computational Biology 11, e1004321 (2015). 2. Keseler, I. M. et al. The EcoCyc database: reflecting new knowledge about _Escherichia coli_ K-12. Nucleic Acids Res 45, D543–D550 (2017).