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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Building a Model
Model, Reactions and Metabolites
This simple example demonstrates how to create a model, create a reaction, and then add the reaction to the model.
We'll use the '3OAS140' reaction from the STM_1.0 model
Step1:  We need to create metabolites as well. If we were using an existing model, we could use Model.get_by_id to get the appropriate Metabolite objects instead.
Step2:  Side note
Step3:  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)
Step4:  At this point in time, the model is still empty
Step5:  We will add the reaction to the model, which will also add all associated metabolites and genes
Step6:  We can iterate through the model objects to observe the contents
Step7:  Objective
Last we need to set the objective of the model. Here, we just want this to be the maximization of the flux in the single reaction we added and we do this by assigning the reaction's identifier to the objective property of the model.
Step8:  The created objective is a symbolic algebraic expression and we can examine it by printing it
Step9:  which here shows that the solver will maximize the flux in the forward direction.
Model Validation
For exchange with other tools you can validate and export the model to SBML.
For more information on serialization and available formats see the section "Reading and Writing Models"
Step10:  The model is valid with no COBRA or SBML errors or warnings.
Exchanges, Sinks and Demands
Boundary reactions can be added using the model's method add_boundary.
There are three different types of pre-defined boundary reactions
Step11:  Boundary reactions are defined on metabolites. First we add two metabolites to the model then
we define the boundary reactions. We add glycogen to the cytosolic compartment c and CO2 to the external compartment e.
Step12:  To create a demand reaction instead of a sink use type demand instead of sink.
Information on all boundary reactions is available via the model's property boundary.
Step13:  A neat trick to get all metabolic reactions is
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Python Code:
from cobra import Model, Reaction, Metabolite
model = Model('example_model')
reaction = Reaction('R_3OAS140')
reaction.name = '3 oxoacyl acyl carrier protein synthase n C140 '
reaction.subsystem = 'Cell Envelope Biosynthesis'
reaction.lower_bound = 0.  # This is the default
reaction.upper_bound = 1000.  # This is the default
Explanation: Building a Model
Model, Reactions and Metabolites
This simple example demonstrates how to create a model, create a reaction, and then add the reaction to the model.
We'll use the '3OAS140' reaction from the STM_1.0 model:
1.0 malACP[c] + 1.0 h[c] + 1.0 ddcaACP[c] $\rightarrow$ 1.0 co2[c] + 1.0 ACP[c] + 1.0 3omrsACP[c]
First, create the model and reaction.
End of explanation
ACP_c = Metabolite(
    'ACP_c',
    formula='C11H21N2O7PRS',
    name='acyl-carrier-protein',
    compartment='c')
omrsACP_c = Metabolite(
    'M3omrsACP_c',
    formula='C25H45N2O9PRS',
    name='3-Oxotetradecanoyl-acyl-carrier-protein',
    compartment='c')
co2_c = Metabolite('co2_c', formula='CO2', name='CO2', compartment='c')
malACP_c = Metabolite(
    'malACP_c',
    formula='C14H22N2O10PRS',
    name='Malonyl-acyl-carrier-protein',
    compartment='c')
h_c = Metabolite('h_c', formula='H', name='H', compartment='c')
ddcaACP_c = Metabolite(
    'ddcaACP_c',
    formula='C23H43N2O8PRS',
    name='Dodecanoyl-ACP-n-C120ACP',
    compartment='c')
Explanation: We need to create metabolites as well. If we were using an existing model, we could use Model.get_by_id to get the appropriate Metabolite objects instead.
End of explanation
reaction.add_metabolites({
    malACP_c: -1.0,
    h_c: -1.0,
    ddcaACP_c: -1.0,
    co2_c: 1.0,
    ACP_c: 1.0,
    omrsACP_c: 1.0
})
reaction.reaction  # This gives a string representation of the reaction
Explanation: Side note: SId
It is highly recommended that the ids for reactions, metabolites and genes are valid SBML identifiers (SId). 
SId is a data type derived from the basic XML typestring, but with restrictions about the characters 
permitted and the sequences in which those characters may appear. 
letter   ::=   ’a’..’z’,’A’..’Z’
digit    ::=   ’0’..’9’
idChar   ::=   letter | digit | ’_’
SId      ::=   ( letter | ’_’ ) idChar*
The main limitation is that ids cannot start with numbers. Using SIds allows serialization to SBML. In addition
features such as code completion and object access via the dot syntax will work in cobrapy.
Adding metabolites to a reaction uses a dictionary of the metabolites and their stoichiometric coefficients. A group of metabolites can be added all at once, or they can be added one at a time.
End of explanation
reaction.gene_reaction_rule = '( STM2378 or STM1197 )'
reaction.genes
Explanation: 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. We will assign the gene reaction rule string, which will automatically create the corresponding gene objects.
End of explanation
print(f'{len(model.reactions)} reactions initially')
print(f'{len(model.metabolites)} metabolites initially')
print(f'{len(model.genes)} genes initially')
Explanation: At this point in time, the model is still empty
End of explanation
model.add_reactions([reaction])
# The objects have been added to the model
print(f'{len(model.reactions)} reactions')
print(f'{len(model.metabolites)} metabolites')
print(f'{len(model.genes)} genes')
Explanation: We will add the reaction to the model, which will also add all associated metabolites and genes
End of explanation
# Iterate through the the objects in the model
print("Reactions")
print("---------")
for x in model.reactions:
    print("%s : %s" % (x.id, x.reaction))
print("")
print("Metabolites")
print("-----------")
for x in model.metabolites:
    print('%9s : %s' % (x.id, x.formula))
print("")
print("Genes")
print("-----")
for x in model.genes:
    associated_ids = (i.id for i in x.reactions)
    print("%s is associated with reactions: %s" %
          (x.id, "{" + ", ".join(associated_ids) + "}"))
Explanation: We can iterate through the model objects to observe the contents
End of explanation
model.objective = 'R_3OAS140'
Explanation: Objective
Last we need to set the objective of the model. Here, we just want this to be the maximization of the flux in the single reaction we added and we do this by assigning the reaction's identifier to the objective property of the model.
End of explanation
print(model.objective.expression)
print(model.objective.direction)
Explanation: The created objective is a symbolic algebraic expression and we can examine it by printing it
End of explanation
import tempfile
from pprint import pprint
from cobra.io import write_sbml_model, validate_sbml_model
with tempfile.NamedTemporaryFile(suffix='.xml') as f_sbml:
    write_sbml_model(model, filename=f_sbml.name)
    report = validate_sbml_model(filename=f_sbml.name)
pprint(report)
Explanation: which here shows that the solver will maximize the flux in the forward direction.
Model Validation
For exchange with other tools you can validate and export the model to SBML.
For more information on serialization and available formats see the section "Reading and Writing Models"
End of explanation
print("exchanges", model.exchanges)
print("demands", model.demands)
print("sinks", model.sinks)
Explanation: The model is valid with no COBRA or SBML errors or warnings.
Exchanges, Sinks and Demands
Boundary reactions can be added using the model's method add_boundary.
There are three different types of pre-defined boundary reactions: exchange, demand, and sink reactions. All of them are unbalanced pseudo reactions, that means they fulfill a function for modeling by adding to or removing metabolites from the model system but are not based on real biology. An exchange reaction is a reversible reaction that adds to or removes an extracellular metabolite from the extracellular compartment. A demand reaction is an irreversible reaction that consumes an intracellular metabolite. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite.
End of explanation
model.add_metabolites([
    Metabolite(
    'glycogen_c',
    name='glycogen',
    compartment='c'
    ),
    Metabolite(
    'co2_e',
    name='CO2',
    compartment='e'
    ),
])
# create exchange reaction
model.add_boundary(model.metabolites.get_by_id("co2_e"), type="exchange")
# create exchange reaction
model.add_boundary(model.metabolites.get_by_id("glycogen_c"), type="sink")
# Now we have an additional exchange and sink reaction in the model
print("exchanges", model.exchanges)
print("sinks", model.sinks)
print("demands", model.demands)
Explanation: Boundary reactions are defined on metabolites. First we add two metabolites to the model then
we define the boundary reactions. We add glycogen to the cytosolic compartment c and CO2 to the external compartment e.
End of explanation
# boundary reactions
model.boundary
Explanation: To create a demand reaction instead of a sink use type demand instead of sink.
Information on all boundary reactions is available via the model's property boundary.
End of explanation
# metabolic reactions
set(model.reactions) - set(model.boundary)
Explanation: A neat trick to get all metabolic reactions is
End of explanation
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