This is the code used in the paper:
Jacobs C, Lambourne L, Xia Y, Segrè D. Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates. PLoS ONE. 2017 Jan. 12(1): e0170164.
There are four scripts to run to generate the FBA model predictions for the cost of knocking out genes, in different media, with different assumptions in the cost calculations.
download_and_process_data.py: Run once to obtain the input data.
send_*_knockouts_to_cluster.py: Calculates growth for all single/double gene knockouts using the Open Grid Scheduler batch system on a computing cluster.
collate_cluster_output.py: Run once after all batch jobs have finished to combine their outputs.
The figures related to the single knockouts are produced in the jupyter notebook examine_correlations.ipynb and the figures related to the double knockouts are produced in epistasis.ipynb.
Uses Python 2.7. Uses the cobrapy package. The FBA requires a linear programming solver.
I've used Gurobi, which requires signing up for an
account on their website before you can install the
software.
The other python package dependancies are listed in requirements.txt.
The code is written to analyze the yeast 7.6 FBA model. Since there are no standardized reaction IDs across different models, you need to edit the carbon_sources.txt and nitrogen_sources.txt files by hand to have the correct names for the exchange reactions for the new model.
To get gurobi working in the scc1 cluster:
module load gurobi
export PYTHONPATH=/share/pkg/gurobi/6.5.2/install/python/python2.7/site-packages:$PYTHONPATH