def mip_score(community, environment=None, min_mass_weight=False, min_growth=1, direction=-1, max_uptake=100, validate=False):
"""
Implements the metabolic interaction potential (MIP) score as defined in (Zelezniak et al, 2015).
Args:
community (Community): microbial community model
environment (Environment): Metabolic environment in which the SMETANA score is calculated
direction (int): direction of uptake reactions (negative or positive, default: -1)
extracellular_id (str): extracellular compartment id
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
Returns:
float: MIP score
"""
# TODO: 1_program_cloneModels.prof
interacting_community = community.copy(copy_models=False, interacting=True, merge_extracellular_compartments=False, create_biomass=True)
noninteracting = community.copy(copy_models=False, interacting=False)
exch_reactions = interacting_community.merged.get_exchange_reactions()
if environment:
environment.apply(interacting_community.merged, inplace=True)
environment.apply(noninteracting.merged, inplace=True)
exch_reactions = set(exch_reactions) - set(environment)
interacting_medium, sol1 = minimal_medium(interacting_community.merged, direction=direction,
exchange_reactions=exch_reactions,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake, validate=validate)
#
# Calculate minimal media for non-interacting community
#
noninteracting_medium, sol2 = minimal_medium(noninteracting.merged,
exchange_reactions=exch_reactions,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake, validate=validate)
if noninteracting_medium is None:
score = None
else:
score = len(noninteracting_medium) - len(interacting_medium)
extras = {'noninteracting_medium': noninteracting_medium, 'interacting_medium': interacting_medium,
'noninteracting_solution': sol2, 'interacting_solution': sol1}
return score, extras
def mro_score(community, environment=None, direction=-1, min_mass_weight=False, min_growth=1, max_uptake=100, validate=False):
"""
Implements the metabolic resource overlap (MRO) score as defined in (Zelezniak et al, 2015).
Args:
community (Community): microbial community model
environment (Environment): Metabolic environment in which the SMETANA score is colulated
direction (int): direction of uptake reactions (negative or positive, default: -1)
extracellular_id (str): extracellular compartment id
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
Returns:
float: MRO score
"""
# TODO: 1_program_cloneModels.prof
inter_community = community.copy(copy_models=False, interacting=True, merge_extracellular_compartments=False, create_biomass=False)
indep_community = inter_community.copy(copy_models=False, interacting=False, create_biomass=True)
exch_reactions = set(inter_community.merged.get_exchange_reactions()) - set([inter_community.merged.biomass_reaction])
if environment:
environment.apply(inter_community.merged, inplace=True)
environment.apply(indep_community.merged, inplace=True)
exch_reactions = exch_reactions - set(environment)
noninteracting_medium, sol = minimal_medium(indep_community.merged,
exchange_reactions=exch_reactions,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake, validate=validate)
solutions = [sol]
if sol.status != Status.OPTIMAL:
raise RuntimeError('Failed to find a valid solution')
# anabiotic environment is limited to non-interacting community minimal media
noninteracting_exch = set(noninteracting_medium)
minimal_medium_set = noninteracting_medium | set(environment)
indep_environment = Environment.from_reactions(minimal_medium_set, max_uptake=max_uptake)
indep_environment.apply(inter_community.merged, inplace=True)
individual_media = {}
for org_id in inter_community.organisms:
biomass_reaction = inter_community.organisms_biomass_reactions[org_id]
inter_community.merged.biomass_reaction = biomass_reaction
org_noninteracting_exch = inter_community.organisms_exchange_reactions[org_id]
medium, sol = minimal_medium(inter_community.merged,
exchange_reactions=org_noninteracting_exch,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake, validate=validate)
solutions.append(sol)
if sol.status != Status.OPTIMAL:
raise RuntimeError('Failed to find a valid solution')
individual_media[org_id] = {org_noninteracting_exch[r].original_metabolite for r in medium}
pairwise = {(o1, o2): individual_media[o1] & individual_media[o2] for o1, o2 in combinations(community.organisms, 2)}
numerator = len(individual_media) * sum(map(len, pairwise.values()))
denominator = float(len(pairwise) * sum(map(len, individual_media.values())))
score = numerator / denominator if denominator != 0 else None
extras = {'noninteracting_medium': noninteracting_medium, 'individual_media': individual_media,
'pairwise': pairwise, 'solutions': solutions}
return score, extras
def metabolite_uptake_score(community, environment, min_mass_weight=True, min_growth=1.0, max_uptake=100.0, abstol=1e-6, validate=False, n_solutions=100):
"""
Calculate frequency of metabolite requirement for species growth
Zelezniak A. et al, Metabolic dependencies drive species co-occurrence in diverse microbial communities (PNAS 2015)
Args:
community (Community): community object
environment (Environment): Metabolic environment in which the SMETANA score is colulated
min_mass_weight: Prefer simpler compounds
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
abstol (float): tolerance for detecting a non-zero exchange flux (default: 1e-6)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
n_solutions (int): How many unique solutions to calculate (default: 100)
Returns:
dict: Keys are dependant model names, values are dictionaries with required compounds frequencies
dict: Extra information
"""
interacting_community = community.copy(copy_models=False, interacting=True, create_biomass=False,
merge_extracellular_compartments=False)
environment.apply(interacting_community.merged, inplace=True)
rxn2met = {ex.organism_reaction: ex.original_metabolite
for org_exchanges in interacting_community.organisms_exchange_reactions.itervalues()
for ex in org_exchanges.itervalues()}
m_rxns = interacting_community.merged.reactions
media_metabolites = {met
for exch_id in interacting_community.merged.get_exchange_reactions()
for met in interacting_community.merged.reactions[exch_id].stoichiometry
if exch_id in m_rxns and m_rxns[exch_id].lb < 0}
solutions = []
scores = {}
extras = {'dependencies': {}}
for org_id, exchange_rxns in community.organisms_exchange_reactions.iteritems():
biomass_reaction = interacting_community.organisms_biomass_reactions[org_id]
interacting_community.merged.biomass_reaction = biomass_reaction
# Remove metabolites present in the medium from the list of uptake candidates
exchange_rxns = {rxn_id for rxn_id, cnm in exchange_rxns.iteritems()
if cnm.extracellular_metabolite not in media_metabolites}
medium_list, sol = minimal_medium(interacting_community.merged,
exchange_reactions=exchange_rxns,
direction=-1,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
n_solutions=n_solutions,
max_uptake=max_uptake, validate=validate, abstol=abstol)
solutions.append(sol)
if medium_list:
medium_list = map(lambda medium: [rxn2met[rxn_id] for rxn_id in medium], medium_list)
medium_list_n = float(len(medium_list))
scores[org_id] = {m: count / medium_list_n for m, count in Counter(chain(*medium_list)).iteritems()}
else:
scores[org_id] = {} if sol.status == Status.OPTIMAL else None
extras['dependencies'][org_id] = medium_list
return scores, extras
def mro_score(community,
environment=None,
direction=-1,
min_mass_weight=False,
min_growth=0.1,
max_uptake=10,
validate=False,
verbose=True,
exclude=None):
"""
Implements the metabolic resource overlap (MRO) score as defined in (Zelezniak et al, 2015).
Args:
community (Community): microbial community model
environment (Environment): Metabolic environment in which the SMETANA score is colulated
direction (int): direction of uptake reactions (negative or positive, default: -1)
extracellular_id (str): extracellular compartment id
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 0.1)
max_uptake (float): maximum uptake rate (default: 10)
Returns:
float: MRO score
"""
noninteracting = community.copy(copy_models=False,
interacting=False,
create_biomass=True)
exch_reactions = set(community.merged.get_exchange_reactions())
if environment:
environment.apply(community.merged, inplace=True, warning=False)
environment.apply(noninteracting.merged, inplace=True, warning=False)
exch_reactions &= set(environment)
noninteracting_medium, sol = minimal_medium(
noninteracting.merged,
exchange_reactions=exch_reactions,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake,
validate=validate,
warnings=verbose)
solutions = [sol]
if sol.status != Status.OPTIMAL:
if verbose:
warn(
'MRO: Failed to find a valid solution for non-interacting community'
)
return None, None
# anabiotic environment is limited to non-interacting community minimal media
noninteracting_exch = set(noninteracting_medium)
noninteracting_env = Environment.from_reactions(noninteracting_exch,
max_uptake=max_uptake)
noninteracting_env.apply(community.merged, inplace=True)
individual_media = {}
if exclude is not None:
exclude = {'M_{}_e'.format(x) for x in exclude}
else:
exclude = {}
solver = solver_instance(community.merged)
for org_id in community.organisms:
biomass_reaction = community.organisms_biomass_reactions[org_id]
community.merged.biomass_reaction = biomass_reaction
org_noninteracting_exch = community.organisms_exchange_reactions[
org_id]
medium, sol = minimal_medium(
community.merged,
exchange_reactions=org_noninteracting_exch,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake,
validate=validate,
solver=solver,
warnings=verbose)
solutions.append(sol)
if sol.status != Status.OPTIMAL:
warn('MRO: Failed to find a valid solution for: ' + org_id)
return None, None
individual_media[org_id] = {
org_noninteracting_exch[r].original_metabolite
for r in medium
} - exclude
pairwise = {(o1, o2): individual_media[o1] & individual_media[o2]
for o1, o2 in combinations(community.organisms, 2)}
numerator = len(individual_media) * sum(map(len, pairwise.values()))
denominator = float(
len(pairwise) * sum(map(len, individual_media.values())))
score = numerator / denominator if denominator != 0 else None
extras = {
'noninteracting_medium': noninteracting_medium,
'individual_media': individual_media,
'pairwise': pairwise,
'solutions': solutions
}
return score, extras
def mip_score(community,
environment=None,
min_mass_weight=False,
min_growth=0.1,
direction=-1,
max_uptake=10,
validate=False,
verbose=True,
exclude=None):
"""
Implements the metabolic interaction potential (MIP) score as defined in (Zelezniak et al, 2015).
Args:
community (Community): microbial community model
environment (Environment): Metabolic environment in which the SMETANA score is calculated
direction (int): direction of uptake reactions (negative or positive, default: -1)
extracellular_id (str): extracellular compartment id
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 0.1)
max_uptake (float): maximum uptake rate (default: 10)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
Returns:
float: MIP score
"""
noninteracting = community.copy(copy_models=False, interacting=False)
exch_reactions = set(community.merged.get_exchange_reactions())
if environment:
environment.apply(community.merged, inplace=True, warning=False)
environment.apply(noninteracting.merged, inplace=True, warning=False)
exch_reactions &= set(environment)
interacting_medium, sol1 = minimal_medium(
community.merged,
direction=direction,
exchange_reactions=exch_reactions,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake,
validate=validate,
warnings=verbose)
noninteracting_medium, sol2 = minimal_medium(
noninteracting.merged,
exchange_reactions=exch_reactions,
direction=direction,
min_mass_weight=min_mass_weight,
min_growth=min_growth,
max_uptake=max_uptake,
validate=validate,
warnings=verbose)
if noninteracting_medium is None:
if verbose:
warn(
'MIP: Failed to find a valid solution for non-interacting community'
)
return None, None
else:
if exclude is not None:
exclude_rxns = {'R_EX_M_{}_e_pool'.format(x) for x in exclude}
interacting_medium = set(interacting_medium) - exclude_rxns
noninteracting_medium = set(noninteracting_medium) - exclude_rxns
score = len(noninteracting_medium) - len(interacting_medium)
extras = {
'noninteracting_medium': noninteracting_medium,
'interacting_medium': interacting_medium,
'noninteracting_solution': sol2,
'interacting_solution': sol1
}
return score, extras
def metabolite_uptake_score(community,
environment=None,
min_mass_weight=False,
min_growth=0.1,
max_uptake=10.0,
abstol=1e-6,
validate=False,
n_solutions=100,
pool_gap=0.5,
verbose=True,
exclude=None): #TODO: implement excluded
"""
Calculate frequency of metabolite requirement for species growth
Zelezniak A. et al, Metabolic dependencies drive species co-occurrence in diverse microbial communities (PNAS 2015)
Args:
community (Community): microbial community
environment (Environment): metabolic environment
min_mass_weight (bool): Prefer smaller compounds (default: False)
min_growth (float): minimum growth rate (default: 0.1)
max_uptake (float): maximum uptake rate (default: 10)
abstol (float): tolerance for detecting a non-zero exchange flux (default: 1e-6)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
n_solutions (int): number of alternative solutions to calculate (default: 100)
Returns:
dict: Keys are organism names, values are dictionaries with metabolite frequencies
dict: Extra information
"""
if environment:
environment.apply(community.merged, inplace=True, warning=False)
scores = {}
solver = solver_instance(community.merged)
for org_id, exchange_rxns in community.organisms_exchange_reactions.items(
):
biomass_reaction = community.organisms_biomass_reactions[org_id]
community.merged.biomass_reaction = biomass_reaction
medium_list, sols = minimal_medium(
community.merged,
exchange_reactions=exchange_rxns.keys(),
min_mass_weight=min_mass_weight,
min_growth=min_growth,
n_solutions=n_solutions,
max_uptake=max_uptake,
validate=validate,
abstol=abstol,
use_pool=True,
pool_gap=pool_gap,
solver=solver,
warnings=verbose)
if medium_list:
counter = Counter(chain(*medium_list))
scores[org_id] = {
cnm.original_metabolite: counter[ex] / float(len(medium_list))
for ex, cnm in exchange_rxns.items()
}
else:
if verbose:
warn('MUS: Failed to find a minimal growth medium for ' +
org_id)
scores[org_id] = None
return scores
