def _growth(args):
p, tradeoff, medium, weights, atol, rtol = args
com = load_pickle(p)
if atol is None:
atol = com.solver.configuration.tolerances.optimality
if rtol is None:
rtol = com.solver.configuration.tolerances.optimality
com = load_pickle(p)
if "glpk" in interface_to_str(com.solver.interface):
logger.error(
"Community models were not built with a QP-capable solver. "
"This means that you did not install CPLEX or Gurobi. "
"If you did install one of the two please file a bug report "
"at https://github.com/micom-dev/micom/issues."
)
return None
ex_ids = [r.id for r in com.exchanges]
logger.info(
"%d/%d import reactions found in model.",
medium.index.isin(ex_ids).sum(),
len(medium),
)
com.medium = medium[medium.index.isin(ex_ids)]
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=tradeoff)
rates = sol.members
rates["taxon"] = rates.index
rates["tradeoff"] = tradeoff
rates["sample_id"] = com.id
except Exception:
logger.warning(
"Could not solve cooperative tradeoff for %s. "
"This can often be fixed by chosing ore permissive atol and rtol "
"arguments." % com.id)
return None
# Get the minimal medium and the solution at the same time
sol = minimal_medium(
com,
exchanges=None,
community_growth=sol.growth_rate,
min_growth=rates.growth_rate.drop("medium"),
solution=True,
weights=weights,
atol=atol,
rtol=rtol
)["solution"]
fluxes = sol.fluxes.loc[:, sol.fluxes.columns.str.startswith("EX_")].copy()
fluxes["sample_id"] = com.id
fluxes["tolerance"] = atol
anns = annotate_metabolites_from_exchanges(com)
return {"growth": rates, "exchanges": fluxes, "annotations": anns}
def _growth(args):
p, tradeoff, medium = args
com = load_pickle(p)
ex_ids = [r.id for r in com.exchanges]
logger.info(
"%d/%d import reactions found in model.",
medium.index.isin(ex_ids).sum(),
len(medium),
)
com.medium = medium[medium.index.isin(ex_ids)]
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=tradeoff)
rates = sol.members
rates["taxon"] = rates.index
rates["tradeoff"] = tradeoff
rates["sample_id"] = com.id
except Exception:
logger.warning("Could not solve cooperative tradeoff for %s." % com.id)
return None
# Get the minimal medium
med = minimal_medium(com, 0.95 * sol.growth_rate)
# Apply medium and reoptimize
com.medium = med[med > 0]
sol = com.cooperative_tradeoff(fraction=tradeoff, fluxes=True, pfba=False)
fluxes = sol.fluxes.loc[:, sol.fluxes.columns.str.startswith("EX_")].copy()
fluxes["sample_id"] = com.id
return {"growth": rates, "exchanges": fluxes}
def _fix_medium(args):
"""Get the fixed medium for a model."""
sid, p, min_growth, max_import, min_c, medium, weights = args
com = load_pickle(p)
try:
fixed = mm.complete_medium(com,
medium,
min_growth=min_growth,
max_import=max_import,
minimize_components=min_c,
weights=weights)
except Exception:
logger.warning("Can't reach the specified growth rates for model %s." %
sid)
return None
fixed = pd.DataFrame({"reaction": fixed.index, "flux": fixed.values})
fixed["metabolite"] = [
list(com.reactions.get_by_id(r).metabolites.keys())[0].id
for r in fixed.reaction
]
fixed["description"] = [
list(com.reactions.get_by_id(r).metabolites.keys())[0].name
for r in fixed.reaction
]
fixed["sample_id"] = sid
return fixed
def _tradeoff(args):
p, tradeoffs, medium = args
com = load_pickle(p)
ex_ids = [r.id for r in com.exchanges]
logger.info(
"%d/%d import reactions found in model.",
medium.index.isin(ex_ids).sum(),
len(medium),
)
com.medium = medium[medium.index.isin(ex_ids)]
sol = com.optimize()
rates = sol.members
rates["taxon"] = rates.index
rates["tradeoff"] = np.nan
rates["sample_id"] = com.id
df = [rates]
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=tradeoffs)
except Exception as e:
logger.warning("Sample %s could not be optimized\n %s" %
(com.id, str(e)))
return None
for i, s in enumerate(sol.solution):
rates = s.members
rates["taxon"] = rates.index
rates["tradeoff"] = sol.tradeoff[i]
rates["sample_id"] = com.id
df.append(rates)
df = pd.concat(df)
return df[df.taxon != "medium"]
def growth_rates(sam):
com = load_pickle("data/models/" + sam + ".pickle")
sol = com.optimize()
rates = sol.members
rates["tradeoff"] = np.nan
rates["sample"] = sam
df = [rates]
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=tradeoffs)
except Exception as e:
logger.warning("Sample %s could not be optimized\n %s" % (sam, str(e)))
return pd.DataFrame({
"tradeoff": tradeoffs,
"growth_rate": np.nan,
"sample": sam
})
for i, s in enumerate(sol.solution):
rates = s.members
rates["tradeoff"] = sol.tradeoff[i]
rates["sample"] = sam
df.append(rates)
df = pd.concat(df)
return df
def _medium(args):
"""Get minimal medium for a single model."""
p, min_growth = args
com = load_pickle(p)
medium = mm.minimal_medium(com, 0.0, min_growth=min_growth).to_frame()
medium.columns = ["flux"]
medium.index.name = "reaction"
return medium.reset_index()
def elasticity_worker(sam):
"""Get the exchange elasticities for a sample."""
model_file = "data/models/" + sam + ".pickle"
out_file = "data/elasticities_" + sam + ".csv"
if isfile(out_file):
return
com = load_pickle(model_file)
elast = elasticities(com, fraction=0.5)
elast.to_csv(out_file, index=False)
def elasticities(sam):
"""Get the exchange elasticities for a sample."""
model_file = "models/" + sam + ".pickle"
out_file = "intervention/" + sam + ".csv"
if isfile(out_file):
return
com = load_pickle(model_file)
elast = exchange_elasticities(com, fraction=0.5, progress=False)
elast.to_csv(out_file, index=False)
def test_build():
d = q2m.build(
table, taxa, models.view(q2m._formats_and_types.JSONDirectory), 1, 0.01, True
)
manifest = d.manifest.view(pd.DataFrame)
assert manifest.shape[0] == 3
for sa in manifest.sample_id.unique():
com = micom.load_pickle(d.model_files.path_maker(model_id=sa))
assert isinstance(com, micom.Community)
assert len(com.reactions) > 3 * 95
assert len(com.abundances) == 3
def _medium(args):
"""Get minimal medium for a single model."""
s, p, min_growth = args
com = load_pickle(p)
# open the bounds
for ex in com.exchanges:
ex.bounds = (-1000.0, 1000.0)
try:
medium = mm.minimal_medium(com, 0.0, min_growth=min_growth).to_frame()
except Exception:
return None
medium.columns = ["flux"]
medium["sample_id"] = s
medium.index.name = "reaction"
return medium.reset_index()
def media_and_gcs(sam):
com = load_pickle("data/models/" + sam + ".pickle")
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=0.5)
rates = sol.members["growth_rate"].copy()
rates["community"] = sol.growth_rate
rates.name = sam
except Exception:
logger.warning("Could not solve cooperative tradeoff for %s." % sam)
return None
# Get the minimal medium
med = minimal_medium(com, 0.95 * sol.growth_rate, exports=True)
med.name = sam
# Apply medium and reoptimize
com.medium = med[med > 0]
sol = com.cooperative_tradeoff(fraction=0.5, fluxes=True, pfba=False)
fluxes = sol.fluxes
fluxes["sample"] = sam
return {"medium": med, "gcs": rates, "fluxes": fluxes}
def media_and_gcs(sam):
com = load_pickle(pickles_path +"/"+ sam)
# Get growth rates
try:
sol = com.cooperative_tradeoff(fraction=trade_off)
rates = sol.members["growth_rate"].copy()
rates["community"] = sol.growth_rate
rates.name = sam
except Exception:
logger.warning("Could not solve cooperative tradeoff for %s." % sam)
return None
# Get the minimal medium
med = minimal_medium(com, 0.95 * sol.growth_rate, exports=True)
med.name = sam
# Apply medium and reoptimize
com.medium = med[med > 0]
sol = com.cooperative_tradeoff(fraction=0.5, fluxes=True, pfba=False) # uses the 'classic' FBA instead of the parsimonious FBA
fluxes = sol.fluxes
fluxes["sample"] = sam
return {"medium": med, "gcs": rates, "fluxes": fluxes}
def test_community_pickling(community, tmpdir):
filename = str(tmpdir.join("com.pickle"))
community.to_pickle(filename)
loaded = load_pickle(filename)
assert len(community.reactions) == len(loaded.reactions)
def knockout(sam):
com = load_pickle("data/models/" + sam + ".pickle")
ko = com.knockout_species(fraction=0.5)
ko["sample"] = sam
return ko
# community type and file_paths:
pickles_path = args.comm_fp
sample = args.sample
trade_off = args.trade_off
th = int(args.threads)
out_dir = args.out_folder
#pickles_path = '1_communities'
#sample = 'SRR6784563'
#trade_off = 0.5
#th = 2
# Load community:
comm_fp = pickles_path + "/" + sample + '.pickle'
com = load_pickle(comm_fp)
#############################
#### SIMULATE GROWTH
print("\n simulating growth...\n")
#generate a manifest:
comm_filename = sample + '.pickle'
manifest = pd.DataFrame({
"sample_id": sample,
"file": comm_filename
},
index=[0])
# Medium (western diet, after diluting nutrients absorbed in the small intestine)
med = pd.DataFrame(com.medium.items(), columns=['reaction', 'flux'])
