def get_r_id2fva_bounds(model, threshold=None, rs=None):
r_id2min_max = flux_variability_analysis(model, reaction_list=rs)
r_id2bounds = {}
for r_id, values in r_id2min_max.items():
min_v, max_v = round_value(values['minimum']), round_value(values['maximum'])
if threshold is None or abs(min_v) > threshold or abs(max_v) > threshold:
r_id2bounds[r_id] = min_v, max_v
return r_id2bounds
def get_fluxes_larger_than_threshold(model, threshold=0):
r_id2val = {}
for r_id, value in model.solution.x_dict.items():
value = round_value(value)
if threshold is None or abs(value) > threshold:
r_id2val[r_id] = value
return r_id2val
def get_boundary_metabolites(N, v):
ms = np.dot(N, v)
replace_zeros(ms)
divider = 1.0 * min(chain([0], (abs(it) for it in ms if it)))
if divider:
ms /= divider
return np.array([round_value(it) for it in ms])
def analyse_by_fva(cobra_model, bm_r_id, objective_sense=MAXIMIZE, threshold=0):
cobra_bm_r_id = format_r_id(bm_r_id)
opt_value = optimise_biomass(cobra_model, cobra_bm_r_id, objective_sense, level=logging.DEBUG)
if opt_value:
opt_value = round_value(opt_value)
r_id2bounds = get_r_id2fva_bounds(cobra_model, threshold=threshold)
return r_id2bounds, opt_value
return {}, None
def analyse_by_fba(cobra_model, bm_r_id, objective_sense=MAXIMIZE, threshold=0):
cobra_bm_r_id = format_r_id(bm_r_id)
opt_value = optimise_biomass(cobra_model, cobra_bm_r_id, objective_sense, level=logging.DEBUG)
if opt_value:
opt_value = round_value(opt_value)
r_id2val = get_fluxes_larger_than_threshold(cobra_model, threshold=threshold)
return r_id2val, opt_value
return {}, None
def filter_efms(in_path, r_id2i, rev_r_id2i, out_path, r_id2rev=None, threshold=ZERO_THRESHOLD):
i2r_id = {i: (r_id, False) for (r_id, i) in r_id2i.items()}
i2r_id.update({i: (r_id, True) for (r_id, i) in rev_r_id2i.items()})
efms = []
rejected_bad, rejected_different = 0, 0
get_key = lambda r_id2coeff: \
tuple(sorted(((r_id, coefficient_to_binary(coeff)) for (r_id, coeff) in r_id2coeff.items())))
processed = set()
with open(out_path, 'w+') as out_f:
with open(in_path, 'r') as in_f:
for line in in_f:
values = line.replace("\n", "").strip().split(" ")
r_id2coefficient = {}
bad_em = False
for i, v in enumerate(values, start=1):
v = round_value(v)
if not v or abs(v) <= threshold:
continue
r_id, rev = i2r_id[i]
if rev:
v *= -1
# The same reaction participates in different directions
# => don't want such an EFM
if r_id in r_id2coefficient:
bad_em = True
break
r_id2coefficient[r_id] = v
if bad_em:
rejected_bad += 1
continue
if r_id2rev:
for (r_id, rev) in r_id2rev.items():
if r_id not in r_id2coefficient or (rev is not None and rev != (r_id2coefficient[r_id] < 0)):
rejected_different += 1
bad_em = True
break
if bad_em:
continue
key = get_key(r_id2coefficient)
if key in processed:
continue
processed.add(key)
out_f.write(line)
efms.append(r_id2coefficient)
if rejected_different:
logging.info('Rejected %d EFMs as not all of the reactions of interest were present in them.'
% rejected_different)
if rejected_bad:
logging.info('Rejected %d EFMs as they contained reversible reactions in both directions' % rejected_bad)
return efms
def format_p(p_id):
fm_yield = S.get_yield(p_id, in_m_id, out_m_id)
if fm_yield is not None:
return 'Yield: %g' % round_value(fm_yield)
return ''
