5. Simulating Deletions¶

In [1]:

import pandas
from time import time

import cobra.test
from cobra.flux_analysis import \
    single_gene_deletion, single_reaction_deletion, \
    double_gene_deletion, double_reaction_deletion

cobra_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model("ecoli")

5.1. Single Deletions¶

Perform all single gene deletions on a model

In [2]:

growth_rates, statuses = single_gene_deletion(cobra_model)

These can also be done for only a subset of genes

In [3]:

gr, st = single_gene_deletion(cobra_model,
                              cobra_model.genes[:20])
pandas.DataFrame.from_dict({"growth_rates": gr,
                            "status": st})

Out[3]:

	growth_rates	status
b0116	0.782351	optimal
b0118	0.873922	optimal
b0351	0.873922	optimal
b0356	0.873922	optimal
b0474	0.873922	optimal
b0726	0.858307	optimal
b0727	0.858307	optimal
b1241	0.873922	optimal
b1276	0.873922	optimal
b1478	0.873922	optimal
b1849	0.873922	optimal
b2296	0.873922	optimal
b2587	0.873922	optimal
b3115	0.873922	optimal
b3732	0.374230	optimal
b3733	0.374230	optimal
b3734	0.374230	optimal
b3735	0.374230	optimal
b3736	0.374230	optimal
s0001	0.211141	optimal

This can also be done for reactions

In [4]:

gr, st = single_reaction_deletion(cobra_model,
                                  cobra_model.reactions[:20])
pandas.DataFrame.from_dict({"growth_rates": gr,
                            "status": st}).round(4)

Out[4]:

	growth_rates	status
ACALD	0.8739	optimal
ACALDt	0.8739	optimal
ACKr	0.8739	optimal
ACONTa	0.0000	optimal
ACONTb	0.0000	optimal
ACt2r	0.8739	optimal
ADK1	0.8739	optimal
AKGDH	0.8583	optimal
AKGt2r	0.8739	optimal
ALCD2x	0.8739	optimal
ATPM	0.9166	optimal
ATPS4r	0.3742	optimal
Biomass_Ecoli_core	0.0000	optimal
CO2t	0.4617	optimal
CS	-0.0000	optimal
CYTBD	0.2117	optimal
D_LACt2	0.8739	optimal
ENO	-0.0000	optimal
ETOHt2r	0.8739	optimal
EX_ac_e	0.8739	optimal

5.2. Double Deletions¶

Double deletions run in a similar way. Passing in return_frame=True will cause them to format the results as a pandas Dataframe

In [5]:

double_gene_deletion(cobra_model, cobra_model.genes[-5:],
                     return_frame=True).round(4)

Out[5]:

	b2464	b0008	b2935	b2465	b3919
b2464	0.8739	0.8648	0.8739	0.8739	0.704
b0008	0.8648	0.8739	0.8739	0.8739	0.704
b2935	0.8739	0.8739	0.8739	0.0000	0.704
b2465	0.8739	0.8739	0.0000	0.8739	0.704
b3919	0.7040	0.7040	0.7040	0.7040	0.704

By default, the double deletion function will automatically use multiprocessing, splitting the task over up to 4 cores if they are available. The number of cores can be manually sepcified as well. Setting use of a single core will disable use of the multiprocessing library, which often aids debuggging.

In [6]:

start = time()  # start timer()
double_gene_deletion(ecoli_model, ecoli_model.genes[:300],
                     number_of_processes=2)
t1 = time() - start
print("Double gene deletions for 200 genes completed in "
      "%.2f sec with 2 cores" % t1)

start = time()  # start timer()
double_gene_deletion(ecoli_model, ecoli_model.genes[:300],
                     number_of_processes=1)
t2 = time() - start
print("Double gene deletions for 200 genes completed in "
      "%.2f sec with 1 core" % t2)

print("Speedup of %.2fx" % (t2 / t1))

Double gene deletions for 200 genes completed in 27.03 sec with 2 cores
Double gene deletions for 200 genes completed in 40.73 sec with 1 core
Speedup of 1.51x

Double deletions can also be run for reactions

In [7]:

double_reaction_deletion(cobra_model,
                         cobra_model.reactions[2:7],
                         return_frame=True).round(4)

Out[7]:

	ACKr	ACt2r	ADK1
ACKr	0.8739	0.8739	0.8739
ACONTa	0.0000	0.0000	0.0000
ACONTb	0.0000	0.0000	0.0000
ACt2r	0.8739	0.8739	0.8739
ADK1	0.8739	0.8739	0.8739