import pandas
from time import time
from cobra.io import load_model
from cobra.flux_analysis import (
single_gene_deletion, single_reaction_deletion, double_gene_deletion,
double_reaction_deletion)
cobra_model = load_model("textbook")
ecoli_model = load_model("iJO1366")
A commonly asked question when analyzing metabolic models is what will happen if a certain reaction was not allowed to have any flux at all. This can tested using cobrapy by
print('complete model: ', cobra_model.optimize())
with cobra_model:
cobra_model.reactions.PFK.knock_out()
print('pfk knocked out: ', cobra_model.optimize())
complete model: <Solution 0.874 at 0x7fd813bf3390> pfk knocked out: <Solution 0.704 at 0x7fd813bf31d0>
For evaluating genetic manipulation strategies, it is more interesting to examine what happens if given genes are knocked out as doing so can affect no reactions in case of redundancy, or more reactions if gene when is participating in more than one reaction.
print('complete model: ', cobra_model.optimize())
with cobra_model:
cobra_model.genes.b1723.knock_out()
print('pfkA knocked out: ', cobra_model.optimize())
cobra_model.genes.b3916.knock_out()
print('pfkB knocked out: ', cobra_model.optimize())
complete model: <Solution 0.874 at 0x7fd813bf3c18> pfkA knocked out: <Solution 0.874 at 0x7fd813bf35c0> pfkB knocked out: <Solution 0.704 at 0x7fd813bf3588>
Perform all single gene deletions on a model
deletion_results = single_gene_deletion(cobra_model)
These can also be done for only a subset of genes
single_gene_deletion(cobra_model, cobra_model.genes[:20])
ids | growth | status | |
---|---|---|---|
0 | {b1849} | 0.873922 | optimal |
1 | {b1478} | 0.873922 | optimal |
2 | {b1276} | 0.873922 | optimal |
3 | {b0351} | 0.873922 | optimal |
4 | {b3733} | 0.374230 | optimal |
5 | {b1241} | 0.873922 | optimal |
6 | {b0116} | 0.782351 | optimal |
7 | {b0727} | 0.858307 | optimal |
8 | {b0356} | 0.873922 | optimal |
9 | {b2587} | 0.873922 | optimal |
10 | {b0118} | 0.873922 | optimal |
11 | {b0726} | 0.858307 | optimal |
12 | {b3736} | 0.374230 | optimal |
13 | {b3735} | 0.374230 | optimal |
14 | {b2296} | 0.873922 | optimal |
15 | {b3115} | 0.873922 | optimal |
16 | {b3732} | 0.374230 | optimal |
17 | {b0474} | 0.873922 | optimal |
18 | {s0001} | 0.211141 | optimal |
19 | {b3734} | 0.374230 | optimal |
This can also be done for reactions
single_reaction_deletion(cobra_model, cobra_model.reactions[:20])
ids | growth | status | |
---|---|---|---|
0 | {AKGt2r} | 8.739215e-01 | optimal |
1 | {D_LACt2} | 8.739215e-01 | optimal |
2 | {Biomass_Ecoli_core} | 0.000000e+00 | optimal |
3 | {ETOHt2r} | 8.739215e-01 | optimal |
4 | {ADK1} | 8.739215e-01 | optimal |
5 | {ACONTb} | 3.279963e-17 | optimal |
6 | {ACt2r} | 8.739215e-01 | optimal |
7 | {ACONTa} | 3.344590e-15 | optimal |
8 | {ACKr} | 8.739215e-01 | optimal |
9 | {ALCD2x} | 8.739215e-01 | optimal |
10 | {ATPS4r} | 3.742299e-01 | optimal |
11 | {ACALD} | 8.739215e-01 | optimal |
12 | {ENO} | 1.357454e-16 | optimal |
13 | {EX_ac_e} | 8.739215e-01 | optimal |
14 | {ACALDt} | 8.739215e-01 | optimal |
15 | {CS} | 4.757918e-15 | optimal |
16 | {AKGDH} | 8.583074e-01 | optimal |
17 | {CO2t} | 4.616696e-01 | optimal |
18 | {CYTBD} | 2.116629e-01 | optimal |
19 | {ATPM} | 9.166475e-01 | optimal |
Double deletions run in a similar way.
double_gene_deletion(
cobra_model, cobra_model.genes[-5:]).round(4)
ids | growth | status | |
---|---|---|---|
0 | {b2465, b3919} | 0.7040 | optimal |
1 | {b2935, b2464} | 0.8739 | optimal |
2 | {b0008, b2465} | 0.8739 | optimal |
3 | {b2465} | 0.8739 | optimal |
4 | {b0008} | 0.8739 | optimal |
5 | {b0008, b2464} | 0.8648 | optimal |
6 | {b2465, b2935} | -0.0000 | optimal |
7 | {b0008, b3919} | 0.7040 | optimal |
8 | {b2465, b2464} | 0.8739 | optimal |
9 | {b0008, b2935} | 0.8739 | optimal |
10 | {b3919} | 0.7040 | optimal |
11 | {b2464} | 0.8739 | optimal |
12 | {b2935} | 0.8739 | optimal |
13 | {b2935, b3919} | 0.7040 | optimal |
14 | {b3919, b2464} | 0.7040 | optimal |
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 specified as well. Setting use of a single core will disable use of the multiprocessing library, which often aids debugging.
start = time() # start timer()
double_gene_deletion(
ecoli_model, ecoli_model.genes[:25], 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[:25], 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 1.29 sec with 2 cores Double gene deletions for 200 genes completed in 1.74 sec with 1 core Speedup of 1.35x
Double deletions can also be run for reactions.
double_reaction_deletion(
cobra_model, cobra_model.reactions[2:7]).round(4)
ids | growth | status | |
---|---|---|---|
0 | {ACONTa} | 0.0000 | optimal |
1 | {ACt2r, ACONTa} | 0.0000 | optimal |
2 | {ACKr} | 0.8739 | optimal |
3 | {ACt2r, ACKr} | 0.8739 | optimal |
4 | {ADK1, ACONTb} | 0.0000 | optimal |
5 | {ACONTa, ACONTb} | 0.0000 | optimal |
6 | {ACKr, ACONTa} | 0.0000 | optimal |
7 | {ACKr, ACONTb} | 0.0000 | optimal |
8 | {ACt2r, ADK1} | 0.8739 | optimal |
9 | {ACt2r, ACONTb} | 0.0000 | optimal |
10 | {ADK1, ACKr} | 0.8739 | optimal |
11 | {ACONTb} | 0.0000 | optimal |
12 | {ADK1, ACONTa} | 0.0000 | optimal |
13 | {ACt2r} | 0.8739 | optimal |
14 | {ADK1} | 0.8739 | optimal |
Note that the indices for deletions are python set objects. This is the appropriate type since the order of deletions does not matter. Deleting reaction 1 and reaction 2 will have the same effect as deleting reaction 2 and reaction 1.
To make it easier to access results all DataFrames returned by COBRAPpy deletion functions have a knockout
indexer that makes that a bit simpler. Each entry in the indexer is treated as a single deletion entry. So you need to pass sets for double deletions.
single = single_reaction_deletion(cobra_model)
double = double_reaction_deletion(cobra_model)
print(single.knockout["ATPM"])
print(double.knockout[{"ATPM", "TKT1"}])
ids growth status 12 {ATPM} 0.916647 optimal ids growth status 859 {ATPM, TKT1} 0.90584 optimal
This can be used to get several deletions at once and will also work for Reaction or Gene objects (depending on what you deleted) directly.
atpm = cobra_model.reactions.ATPM
tkt1 = cobra_model.reactions.TKT1
pfk = cobra_model.reactions.PFK
print(single.knockout[atpm, tkt1, pfk])
print(double.knockout[{atpm, tkt1}, {atpm, pfk}, {atpm}])
ids growth status 12 {ATPM} 0.916647 optimal 44 {TKT1} 0.864759 optimal 71 {PFK} 0.704037 optimal ids growth status 425 {ATPM, PFK} 0.704037 optimal 859 {ATPM, TKT1} 0.905840 optimal 4125 {ATPM} 0.916647 optimal