To begin with, cobrapy comes with bundled models for Salmonella and E. coli, as well as a "textbook" model of E. coli core metabolism. To load a test model, type
from __future__ import print_function
import cobra
import cobra.test
# "ecoli" and "salmonella" are also valid arguments
model = cobra.test.create_test_model("textbook")
The reactions, metabolites, and genes attributes of the cobrapy model are a special type of list called a cobra.DictList
, and each one is made up of cobra.Reaction
, cobra.Metabolite
and cobra.Gene
objects respectively.
print(len(model.reactions))
print(len(model.metabolites))
print(len(model.genes))
95 72 137
When using Jupyter notebook this type of information is rendered as a table.
model
Name | e_coli_core |
Memory address | 0x01116ea9e8 |
Number of metabolites | 72 |
Number of reactions | 95 |
Objective expression | -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core |
Compartments | cytosol, extracellular |
Just like a regular list, objects in the DictList
can be retrieved by index. For example, to get the 30th reaction in the model (at index 29 because of 0-indexing):
model.reactions[29]
Reaction identifier | EX_glu__L_e |
Name | L-Glutamate exchange |
Memory address | 0x011b8643c8 |
Stoichiometry |
glu__L_e --> L-Glutamate --> |
GPR | |
Lower bound | 0.0 |
Upper bound | 1000.0 |
Additionally, items can be retrieved by their id
using the DictList.get_by_id()
function. For example, to get the cytosolic atp metabolite object (the id is "atp_c"), we can do the following:
model.metabolites.get_by_id("atp_c")
Metabolite identifier | atp_c |
Name | ATP |
Memory address | 0x011b7f82b0 |
Formula | C10H12N5O13P3 |
Compartment | c |
In 13 reaction(s) | PYK, GLNS, ATPS4r, SUCOAS, PPCK, GLNabc, ATPM, ACKr, Biomass_Ecoli_core, ADK1, PPS, PFK, PGK |
As an added bonus, users with an interactive shell such as IPython will be able to tab-complete to list elements inside a list. While this is not recommended behavior for most code because of the possibility for characters like "-" inside ids, this is very useful while in an interactive prompt:
model.reactions.EX_glc__D_e.bounds
(-10.0, 1000.0)
We will consider the reaction glucose 6-phosphate isomerase, which interconverts glucose 6-phosphate and fructose 6-phosphate. The reaction id for this reaction in our test model is PGI.
pgi = model.reactions.get_by_id("PGI")
pgi
Reaction identifier | PGI |
Name | glucose-6-phosphate isomerase |
Memory address | 0x011b886a90 |
Stoichiometry |
g6p_c <=> f6p_c D-Glucose 6-phosphate <=> D-Fructose 6-phosphate |
GPR | b4025 |
Lower bound | -1000.0 |
Upper bound | 1000.0 |
We can view the full name and reaction catalyzed as strings
print(pgi.name)
print(pgi.reaction)
glucose-6-phosphate isomerase g6p_c <=> f6p_c
We can also view reaction upper and lower bounds. Because the pgi.lower_bound
< 0, and pgi.upper_bound
> 0, pgi
is reversible.
print(pgi.lower_bound, "< pgi <", pgi.upper_bound)
print(pgi.reversibility)
-1000.0 < pgi < 1000.0 True
We can also ensure the reaction is mass balanced. This function will return elements which violate mass balance. If it comes back empty, then the reaction is mass balanced.
pgi.check_mass_balance()
{}
In order to add a metabolite, we pass in a dict
with the metabolite object and its coefficient
pgi.add_metabolites({model.metabolites.get_by_id("h_c"): -1})
pgi.reaction
'g6p_c + h_c <=> f6p_c'
The reaction is no longer mass balanced
pgi.check_mass_balance()
{'H': -1.0, 'charge': -1.0}
We can remove the metabolite, and the reaction will be balanced once again.
pgi.subtract_metabolites({model.metabolites.get_by_id("h_c"): -1})
print(pgi.reaction)
print(pgi.check_mass_balance())
g6p_c <=> f6p_c {}
It is also possible to build the reaction from a string. However, care must be taken when doing this to ensure reaction id's match those in the model. The direction of the arrow is also used to update the upper and lower bounds.
pgi.reaction = "g6p_c --> f6p_c + h_c + green_eggs + ham"
unknown metabolite 'green_eggs' created unknown metabolite 'ham' created
pgi.reaction
'g6p_c --> f6p_c + green_eggs + h_c + ham'
pgi.reaction = "g6p_c <=> f6p_c"
pgi.reaction
'g6p_c <=> f6p_c'
We will consider cytosolic atp as our metabolite, which has the id "atp_c"
in our test model.
atp = model.metabolites.get_by_id("atp_c")
atp
Metabolite identifier | atp_c |
Name | ATP |
Memory address | 0x011b7f82b0 |
Formula | C10H12N5O13P3 |
Compartment | c |
In 13 reaction(s) | PYK, GLNS, ATPS4r, SUCOAS, PPCK, GLNabc, ATPM, ACKr, Biomass_Ecoli_core, ADK1, PPS, PFK, PGK |
We can print out the metabolite name and compartment (cytosol in this case) directly as string.
print(atp.name)
print(atp.compartment)
ATP c
We can see that ATP is a charged molecule in our model.
atp.charge
-4
We can see the chemical formula for the metabolite as well.
print(atp.formula)
C10H12N5O13P3
The reactions attribute gives a frozenset
of all reactions using the given metabolite. We can use this to count the number of reactions which use atp.
len(atp.reactions)
13
A metabolite like glucose 6-phosphate will participate in fewer reactions.
model.metabolites.get_by_id("g6p_c").reactions
frozenset({<Reaction G6PDH2r at 0x11b870c88>, <Reaction GLCpts at 0x11b870f98>, <Reaction PGI at 0x11b886a90>, <Reaction Biomass_Ecoli_core at 0x11b85a5f8>})
The gene_reaction_rule
is a boolean representation of the gene requirements for this reaction to be active as described in Schellenberger et al 2011 Nature Protocols 6(9):1290-307.
The GPR is stored as the gene_reaction_rule for a Reaction object as a string.
gpr = pgi.gene_reaction_rule
gpr
'b4025'
Corresponding gene objects also exist. These objects are tracked by the reactions itself, as well as by the model
pgi.genes
frozenset({<Gene b4025 at 0x11b844cc0>})
pgi_gene = model.genes.get_by_id("b4025")
pgi_gene
Gene identifier | b4025 |
Name | pgi |
Memory address | 0x011b844cc0 |
Functional | True |
In 1 reaction(s) | PGI |
Each gene keeps track of the reactions it catalyzes
pgi_gene.reactions
frozenset({<Reaction PGI at 0x11b886a90>})
Altering the gene_reaction_rule will create new gene objects if necessary and update all relationships.
pgi.gene_reaction_rule = "(spam or eggs)"
pgi.genes
frozenset({<Gene spam at 0x11b850908>, <Gene eggs at 0x11b850eb8>})
pgi_gene.reactions
frozenset()
Newly created genes are also added to the model
model.genes.get_by_id("spam")
Gene identifier | spam |
Name | |
Memory address | 0x011b850908 |
Functional | True |
In 1 reaction(s) | PGI |
The delete_model_genes
function will evaluate the GPR and set the upper and lower bounds to 0 if the reaction is knocked out. This function can preserve existing deletions or reset them using the cumulative_deletions
flag.
cobra.manipulation.delete_model_genes(
model, ["spam"], cumulative_deletions=True)
print("after 1 KO: %4d < flux_PGI < %4d" % (pgi.lower_bound, pgi.upper_bound))
cobra.manipulation.delete_model_genes(
model, ["eggs"], cumulative_deletions=True)
print("after 2 KO: %4d < flux_PGI < %4d" % (pgi.lower_bound, pgi.upper_bound))
after 1 KO: -1000 < flux_PGI < 1000 after 2 KO: 0 < flux_PGI < 0
The undelete_model_genes can be used to reset a gene deletion
cobra.manipulation.undelete_model_genes(model)
print(pgi.lower_bound, "< pgi <", pgi.upper_bound)
-1000 < pgi < 1000
Quite often, one wants to make small changes to a model and evaluate the impacts of these. For example, we may want to knock-out all reactions sequentially, and see what the impact of this is on the objective function. One way of doing this would be to create a new copy of the model before each knock-out with model.copy()
. However, even with small models, this is a very slow approach as models are quite complex objects. Better then would be to do the knock-out, optimizing and then manually resetting the reaction bounds before proceeding with the next reaction. Since this is such a common scenario however, cobrapy allows us to use the model as a context, to have changes reverted automatically.
model = cobra.test.create_test_model('textbook')
for reaction in model.reactions[:5]:
with model as model:
reaction.knock_out()
model.optimize()
print('%s blocked (bounds: %s), new growth rate %f' %
(reaction.id, str(reaction.bounds), model.objective.value))
ACALD blocked (bounds: (0, 0)), new growth rate 0.873922 ACALDt blocked (bounds: (0, 0)), new growth rate 0.873922 ACKr blocked (bounds: (0, 0)), new growth rate 0.873922 ACONTa blocked (bounds: (0, 0)), new growth rate -0.000000 ACONTb blocked (bounds: (0, 0)), new growth rate -0.000000
If we look at those knocked reactions, see that their bounds have all been reverted.
[reaction.bounds for reaction in model.reactions[:5]]
[(-1000.0, 1000.0), (-1000.0, 1000.0), (-1000.0, 1000.0), (-1000.0, 1000.0), (-1000.0, 1000.0)]
Nested contexts are also supported
print('original objective: ', model.objective.expression)
with model:
model.objective = 'ATPM'
print('print objective in first context:', model.objective.expression)
with model:
model.objective = 'ACALD'
print('print objective in second context:', model.objective.expression)
print('objective after exiting second context:',
model.objective.expression)
print('back to original objective:', model.objective.expression)
original objective: -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core print objective in first context: -1.0*ATPM_reverse_5b752 + 1.0*ATPM print objective in second context: 1.0*ACALD - 1.0*ACALD_reverse_fda2b objective after exiting second context: -1.0*ATPM_reverse_5b752 + 1.0*ATPM back to original objective: -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core
Most methods that modify the model are supported like this including adding and removing reactions and metabolites and setting the objective. Supported methods and functions mention this in the corresponding documentation.
While it does not have any actual effect, for syntactic convenience it is also possible to refer to the model by a different name than outside the context. Such as
with model as inner:
inner.reactions.PFK.knock_out