Pangenome reconstruction of Escherichia coli metabolism (E. coli panGEM - Full set) + codes and data for pangenome scale knock-out simulation

*-- Rare Metabolic Gene Essentiality is a Determinant of Microniche Adaptation in Escherichia coli --*

This repository contains Jupyter Notebooks and scripts for analyzing Escherichia coli pan-genome-scale metabolic models (panGEMs) to reproduce the results and figures presented in our manuscript. The primary objectives of these analyses are:

1. Validation of GEMs using:
   • Gene knock-out data
   • BioLog phenotyping data
2. Simulation of E. coli growth in major human body niches (feces, urine, and serum).
3. Detection of available nutrients in each body site that support E. coli growth.
4. Prediction of gene fitness scores across the panGEM using extensive gene knock-out simulations in feces, urine, serum, and M9 minimal media as a standard reference.
5. Identification of rare essential genes across E. coli panGEM.

panGEM Reconstruction & Curation

For reconstruction and curation of panGEMs, please refer to our previously published notebook available at:
🔗 GitHub: EcopanGEM

If you use the scripts for panGEM reconstruction, please cite the following article:
📄 Preprint: BioRxiv

Prediction of Fitness Scores Across Feces, Serum, Urine, and M9 Media

To run the fitness score predictions, execute the following scripts in the terminal.

💡 Note:

• The fitness score prediction is computationally intensive.
• Scripts are optimized for high-performance computing environments (96-core virtual machines).
• Running these scripts on lower configurations may result in prolonged computation times.

Scripts Overview

Media Simulation

📌 media.py – Functions for simulating feces, serum, urine, and M9 minimal media.

Flux Variability Analysis (FVA)

📌 fva_ecoli_biosamples.py – FVA across all reactions of all strains in feces, serum, and urine.
📌 upec_non_upec_feces_fva_comparison.py – FVA comparison between UPEC and non-UPEC strains.
📌 serum_eco.py – FVA across all GEMs on serum medium.
📌 urine_eco.py – FVA across all GEMs on urine medium.
📌 feces_eco.py – FVA across all GEMs on feces medium.

Gene Knock-Out Fitness Score Simulations

📌 fitness_rare_m9.py – Knock-out simulation on M9 medium.
📌 fitness_rare_feces.py – Knock-out simulation on feces medium.
📌 fitness_rare_serum.py – Knock-out simulation on serum medium.
📌 fitness_rare_urine.py – Knock-out simulation on urine medium.

Concatenation of Knock-Out Simulation Results

📌 master_feces_concat.py – Merging results from feces knock-out simulations.
📌 master_m9_concat.py – Merging results from M9 knock-out simulations.
📌 master_serum_concat.py – Merging results from serum knock-out simulations.
📌 master_urine_concat.py – Merging results from urine knock-out simulations.

Data Availability

All data generated in this study are available in this repository, allowing users to reproduce the results without running simulations. This ensures easy validation and reanalysis of our findings.