Scripts to map keffs using iML1515 to iJL1678b keffs. This repository contains all of the resources necessary to reproduce the simulations from "Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers"

The code to reproduce simulations from "Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models" has been moved to the ML_keffs_study1 branch.

The simulated models are then compared (by mass or mole fraction) against the proteomics data from "The quantitative and condition-dependent Escherichia coli proteome"

To run the relevant simulations and validations clone the repository and run:

python run_all_keff_simulations_and_validations.py

this will use solve using qminos in quad precision, to use gurobi v8.1 in low precision edit the solver argument in maximize_growth_rate in run_all_simulations_and_validations.py to:

run_simulations.maximize_growth_rate(model, media, simulation_savefile_name, solver='gurobi', precision=1e-12)

The simulations from run_all_keff_simulations_and_validations.py are output as simulation_validation_output.csv

• Python 3.6
• COBRApy - v0.5.11
• COBRAme - v0.0.9
• ECOLIme - v0.0.9
• solvemepy - v1.0.1
• matplotlib
• pandas
• numpy
• gurobi v8.1 or qminos