def test_find_constrained_pure_metabolic_reactions(model):
"""
Expect zero or more purely metabolic reactions to have fixed constraints.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction. This test
requires the presence of metabolite formula to be able to identify
transport reactions. This test simply reports the number of purely
metabolic reactions that have fixed constraints and does not have any
mandatory 'pass' criteria.
Implementation: From the pool of pure metabolic reactions identify
reactions which are constrained to values other than the model's minimal or
maximal possible bounds.
"""
ann = test_find_constrained_pure_metabolic_reactions.annotation
pmr = basic.find_pure_metabolic_reactions(model)
ann["data"] = get_ids_and_bounds(
[rxn for rxn in pmr if basic.is_constrained_reaction(
model, rxn)])
ann["metric"] = len(ann["data"]) / len(pmr)
ann["message"] = wrapper.fill(
"""A total of {:d} ({:.2%}) purely metabolic reactions have fixed
constraints in the model, this excludes transporters, exchanges, or
pseudo-reactions: {}""".format(len(ann["data"]), ann["metric"],
truncate(ann["data"])))
def test_metabolic_reaction_specific_sbo_presence(read_only_model):
"""Expect all metabolic reactions to be annotated with SBO:0000176.
SBO:0000176 represents the term 'biochemical reaction'. Every metabolic
reaction that is not a transport or boundary reaction should be annotated
with this. The results shown are relative to the total amount of pure
metabolic reactions.
"""
ann = test_metabolic_reaction_specific_sbo_presence.annotation
pure = basic.find_pure_metabolic_reactions(read_only_model)
ann["data"] = get_ids(sbo.check_component_for_specific_sbo_term(
pure, "SBO:0000176"))
try:
ann["metric"] = len(ann["data"]) / len(pure)
ann["message"] = wrapper.fill(
"""A total of {} metabolic reactions ({:.2%} of all purely
metabolic reactions) lack annotation with the SBO term
"SBO:0000176" for 'biochemical reaction': {}""".format(
len(ann["data"]), ann["metric"], truncate(ann["data"])))
except ZeroDivisionError:
ann["metric"] = 1.0
ann["message"] = "The model has no metabolic reactions."
pytest.skip(ann["message"])
assert len(ann["data"]) == len(pure), ann["message"]
def test_find_constrained_pure_metabolic_reactions(model):
"""
Expect zero or more purely metabolic reactions to have fixed constraints.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction. This test
requires the presence of metabolite formula to be able to identify
transport reactions. This test simply reports the number of purely
metabolic reactions that have fixed constraints and does not have any
mandatory 'pass' criteria.
Implementation: From the pool of pure metabolic reactions identify
reactions which are constrained to values other than the model's minimal or
maximal possible bounds.
"""
ann = test_find_constrained_pure_metabolic_reactions.annotation
pmr = basic.find_pure_metabolic_reactions(model)
ann["data"] = get_ids_and_bounds(
[rxn for rxn in pmr if basic.is_constrained_reaction(model, rxn)])
ann["metric"] = len(ann["data"]) / len(pmr)
ann["message"] = wrapper.fill(
"""A total of {:d} ({:.2%}) purely metabolic reactions have fixed
constraints in the model, this excludes transporters, exchanges, or
pseudo-reactions: {}""".format(len(ann["data"]), ann["metric"],
truncate(ann["data"])))
def test_find_pure_metabolic_reactions(model):
"""
Expect at least one pure metabolic reaction to be defined in the model.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction. This test
requires the presence of metabolite formula to be able to identify
transport reactions. This test is passed when the model contains at least
one purely metabolic reaction i.e. a conversion of one metabolite into
another.
Implementation:
From the list of all reactions, those that are boundary, transport and
biomass reactions are removed and the remainder assumed to be pure
metabolic reactions. Boundary reactions are identified using the attribute
cobra.Model.boundary. Please read the description of "Transport Reactions"
and "Biomass Reaction Identified" to learn how they are identified.
"""
ann = test_find_pure_metabolic_reactions.annotation
ann["data"] = get_ids(
basic.find_pure_metabolic_reactions(model))
ann["metric"] = len(ann["data"]) / len(model.reactions)
ann["message"] = wrapper.fill(
"""A total of {:d} ({:.2%}) purely metabolic reactions are defined in
the model, this excludes transporters, exchanges, or pseudo-reactions:
{}""".format(len(ann["data"]), ann["metric"], truncate(ann["data"])))
assert len(ann["data"]) >= 1, ann["message"]
def test_find_pure_metabolic_reactions(model):
"""
Expect at least one pure metabolic reaction to be defined in the model.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction. This test
requires the presence of metabolite formula to be able to identify
transport reactions. This test is passed when the model contains at least
one purely metabolic reaction i.e. a conversion of one metabolite into
another.
Implementation:
From the list of all reactions, those that are boundary, transport and
biomass reactions are removed and the remainder assumed to be pure
metabolic reactions. Boundary reactions are identified using the attribute
cobra.Model.boundary. Please read the description of "Transport Reactions"
and "Biomass Reaction Identified" to learn how they are identified.
"""
ann = test_find_pure_metabolic_reactions.annotation
ann["data"] = get_ids(basic.find_pure_metabolic_reactions(model))
ann["metric"] = len(ann["data"]) / len(model.reactions)
ann["message"] = wrapper.fill(
"""A total of {:d} ({:.2%}) purely metabolic reactions are defined in
the model, this excludes transporters, exchanges, or pseudo-reactions:
{}""".format(len(ann["data"]), ann["metric"], truncate(ann["data"])))
assert len(ann["data"]) >= 1, ann["message"]
def test_metabolic_reaction_specific_sbo_presence(model):
"""Expect all metabolic reactions to be annotated with SBO:0000176.
SBO:0000176 represents the term 'biochemical reaction'. Every metabolic
reaction that is not a transport or boundary reaction should be annotated
with this. The results shown are relative to the total amount of pure
metabolic reactions.
Implementation:
Check if each pure metabolic reaction has a non-zero "annotation"
attribute that contains the key "sbo" with the associated
value being the SBO term above.
"""
ann = test_metabolic_reaction_specific_sbo_presence.annotation
pure = basic.find_pure_metabolic_reactions(model)
ann["data"] = get_ids(sbo.check_component_for_specific_sbo_term(
pure, "SBO:0000176"))
try:
ann["metric"] = len(ann["data"]) / len(pure)
ann["message"] = wrapper.fill(
"""A total of {} metabolic reactions ({:.2%} of all purely
metabolic reactions) lack annotation with the SBO term
"SBO:0000176" for 'biochemical reaction': {}""".format(
len(ann["data"]), ann["metric"], truncate(ann["data"])))
except ZeroDivisionError:
ann["metric"] = 1.0
ann["message"] = "The model has no metabolic reactions."
pytest.skip(ann["message"])
assert len(ann["data"]) == len(pure), ann["message"]
def test_find_pure_metabolic_reactions(read_only_model, store):
"""Expect >= 1 pure metabolic reactions are present in the model."""
store["metabolic_reactions"] = [
rxn.id for rxn in basic.find_pure_metabolic_reactions(read_only_model)
]
store["num_metabolic_reactions"] = len(store["metabolic_reactions"])
assert store["num_metabolic_reactions"] >= 1
def test_find_pure_metabolic_reactions(read_only_model):
"""
Expect at least one pure metabolic reaction to be defined in the model.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction. This test
requires the presence of metabolite formula to be able to identify
transport reactions. This test is passed when the model contains at least
one purely metabolic reaction i.e. a conversion of one metabolite into
another.
"""
ann = test_find_pure_metabolic_reactions.annotation
ann["data"] = get_ids(basic.find_pure_metabolic_reactions(read_only_model))
ann["metric"] = len(ann["data"]) / len(read_only_model.reactions)
ann["message"] = wrapper.fill(
"""A total of {:d} ({:.2%}) purely metabolic reactions are defined in
the model, this excludes transporters, exchanges, or pseudo-reactions:
{}""".format(len(ann["data"]), ann["metric"], truncate(ann["data"])))
assert len(ann["data"]) >= 1, ann["message"]
def test_find_constrained_pure_metabolic_reactions(model, num):
"""Expect num of contrained metabolic rxns to be identified correctly."""
pmr = basic.find_pure_metabolic_reactions(model)
contrained_pmr = set(
[rxn for rxn in pmr if basic.is_constrained_reaction(model, rxn)])
assert len(contrained_pmr) == num
def test_find_pure_metabolic_reactions(model, num):
"""Expect amount of metabolic reactions to be identified correctly."""
assert len(basic.find_pure_metabolic_reactions(model)) == num
def test_find_candidate_irreversible_reactions(model):
u"""
Identify reversible reactions that could be irreversible.
If a reaction is neither a transport reaction, a biomass reaction nor a
boundary reaction, it is counted as a purely metabolic reaction.
This test checks if the reversibility attribute of each reaction
agrees with a thermodynamics-based
calculation of reversibility.
Implementation:
To determine reversibility we calculate
the reversibility index ln_gamma (natural logarithm of gamma) of each
reaction
using the eQuilibrator API. We consider reactions, whose reactants'
concentrations would need to change by more than three orders of
magnitude for the reaction flux to reverse direction, to be likely
candidates of irreversible reactions. This assume default concentrations
around 100 μM (~3 μM—3 mM) at pH = 7, I = 0.1 M and T = 298 K. The
corresponding reversibility index is approximately 7. For
further information on the thermodynamic and implementation details
please refer to
https://doi.org/10.1093/bioinformatics/bts317 and
https://pypi.org/project/equilibrator-api/.
Please note that currently eQuilibrator can only determine the
reversibility index for chemically and redox balanced reactions whose
metabolites can be mapped to KEGG compound identifiers (e.g. C00001). In
addition
to not being mappable to KEGG or the reaction not being balanced,
there is a possibility that the metabolite cannot be broken down into
chemical groups which is essential for the calculation of Gibbs energy
using group contributions. This test collects each erroneous reaction
and returns them as a tuple containing each list in the following order:
1. Reactions with reversibility index
2. Reactions with incomplete mapping to KEGG
3. Reactions with metabolites that are problematic during calculation
4. Chemically or redox unbalanced Reactions (after mapping to KEGG)
This test simply reports the number of reversible reactions that, according
to the reversibility index, are likely to be irreversible.
"""
# With gamma = 1000, ln_gamma ~ 6.9. We use 7 as the cut-off.
threshold = 7.0
ann = test_find_candidate_irreversible_reactions.annotation
met_rxns = basic.find_pure_metabolic_reactions(model)
rev_index, incomplete, problematic, unbalanced = \
thermo.find_thermodynamic_reversibility_index(met_rxns)
ann["data"] = (
# The reversibility index can be infinite so we convert it to a JSON
# compatible string.
[(r.id, str(i)) for r, i in rev_index],
get_ids(incomplete),
get_ids(problematic),
get_ids(unbalanced)
)
num_irrev = sum(1 for r, i in rev_index if abs(i) >= threshold)
ann["message"] = wrapper.fill(
"""Out of {} purely metabolic reactions, {} have an absolute
reversibility index greater or equal to 7 and are therefore likely
candidates for being irreversible.
{} reactions could not be mapped to KEGG completely, {} contained
'problematic' metabolites, and {} are chemically or redox imbalanced.
""".format(len(met_rxns), num_irrev, len(incomplete), len(problematic),
len(unbalanced))
)
ann["metric"] = num_irrev / len(rev_index)
