def elasticities_by_medium(com, reactions, fraction, growth_rate, progress):
"""Get the elasticity coefficients for a set of variables.
Arguments
---------
com : micom.Community
The community for wrhich to calculate elasticities.
variables : list of optlang.Variable
The variables for which to calculate the elasticities. All of these
must have non-zero primal vaues in the previous solution.
Returns
-------
pandas.Dataframe
The long/tidy version of the elasticities. Contains columns variable,
effector, and elasticity.
"""
regularize_l2_norm(com, 0.0)
sol = optimize_with_fraction(com, fraction, growth_rate, True)
before = _get_fluxes(sol, reactions)
import_fluxes = pd.Series()
dfs = []
for ex in com.exchanges:
export = len(ex.reactants) == 1
flux = sol.fluxes.loc[ex.community_id, ex.global_id]
if export and (flux < -1e-6):
import_fluxes[ex] = flux
elif not export and (flux > 1e-6):
import_fluxes[ex] = -flux
else:
continue
fluxes = import_fluxes.index
if progress:
fluxes = track(fluxes, description="Metabolites")
for r in fluxes:
flux = import_fluxes[r]
with com:
if flux < -1e-6:
r.lower_bound *= np.exp(STEP)
else:
r.upper_bound *= np.exp(STEP)
sol = optimize_with_fraction(com, fraction, growth_rate, True)
after = _get_fluxes(sol, reactions)
deriv, dirs = _derivatives(before, after)
res = pd.DataFrame({
"reaction": [rx.global_id for rx in reactions],
"taxon": [list(r.compartments)[0] for r in reactions],
"effector":
r.id,
"direction":
dirs,
"elasticity":
deriv,
})
dfs.append(res)
return pd.concat(dfs)
def elasticities_by_abundance(com, reactions, fraction, growth_rate, progress):
"""Get the elasticity coefficients for a set of variables.
Arguments
---------
com : micom.Community
The community for which to calculate elasticities.
variables : list of optlang.Variable
The variables for which to calculate the elasticities. All of these
must have non-zero primal vaues in the previous solution.
Returns
-------
pandas.Dataframe
The long/tidy version of the elasticities. Contains columns variable,
effector, and elasticity.
"""
regularize_l2_norm(com, 0.0)
sol = optimize_with_fraction(com, fraction, growth_rate, True)
before = _get_fluxes(sol, reactions)
dfs = []
abundance = com.abundances.copy()
taxa = abundance.index
if progress:
taxa = track(taxa, description="Taxa")
for sp in taxa:
old = abundance[sp]
abundance.loc[sp] *= np.exp(STEP)
com.set_abundance(abundance, normalize=False)
sol = optimize_with_fraction(com, fraction, growth_rate, True)
after = _get_fluxes(sol, reactions)
abundance.loc[sp] = old
com.set_abundance(abundance, normalize=False)
deriv, dirs = _derivatives(before, after)
res = pd.DataFrame({
"reaction": [r.global_id for r in reactions],
"taxon": [list(r.compartments)[0] for r in reactions],
"effector":
sp,
"direction":
dirs,
"elasticity":
deriv,
})
dfs.append(res)
return pd.concat(dfs)
def knockout_taxa(
community, taxa, fraction, method, progress, diag=True
):
"""Knockout a taxon from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.taxa)
_apply_min_growth(com, min_growth)
com.objective = com.scale * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.")
/ com.scale
)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
iter = track(taxa, description="Knockouts") if progress else taxa
for sp in iter:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out()
for r in com.reactions.query(
lambda ri: ri.community_id == sp
)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=taxa).drop("medium", 1)
ko = ko.loc[ko.index.sort_values(), ko.columns.sort_values()]
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def knockout_species(community,
species,
fraction,
method,
progress,
diag=True):
"""Knockout a species from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.species)
_apply_min_growth(com, min_growth)
com.objective = 1000.0 * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.") /
1000.0)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
if progress:
species = tqdm(species, unit="knockout(s)")
for sp in species:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out() for r in com.reactions.query(
lambda ri: ri.community_id == sp)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=species).drop("medium", 1)
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def test_fraction(community):
sol = ms.optimize_with_fraction(community, 0.5)
assert sol.growth_rate == approx(0.874, 0.001)