def get_initial_system(model, res_dir, r_id2rev, tree_efm_path, max_efm_number, threshold, rewrite=True,
pw2rs=None):
m_id2i, r_id2i = get_element2id_mapping(model)
boundary_m_ids = [m.getId() for m in model.getListOfSpecies() if m.getBoundaryCondition()]
N = model2stoichiometric_matrix(model, m_id2i, r_id2i)
logging.info('Calculated N of shape %s' % 'x'.join((str(it) for it in N.shape)))
r_id2coefficient_list = compute_efms(model, res_dir, max_efm_number, r_id2rev=r_id2rev,
tree_efm_path=tree_efm_path, threshold=threshold, rewrite=rewrite)
efm_id2pws = {'efm_%d' % i: r_ids2pws(set(r_id2coeff.keys()), pw2rs)
for i, r_id2coeff in enumerate(r_id2coefficient_list)}
V = get_efm_matrix(r_id2coefficient_list, r_id2i)
# inner_m_ids = [m.getId() for m in model.getListOfSpecies() if not m.getBoundaryCondition()]
# rev = [model.getReaction(r_id).getReversible() for (r_id, i) in
# sorted(r_id2i.items(), key=lambda (_, i): i)]
# N_in = N[[m_id2i[m_id] for m_id in inner_m_ids], :]
# V = sampler(N_in, rev, K=None, debug=True)
# V = V[:, [i for i in range(0, V.shape[1])
# if V[r_id2i[out_r_id], i] * (-1 if out_rev else 1) > 0 and
# next((False for (in_r_id, in_rev) in in_r_id2rev.items() if
# V[r_id2i[in_r_id], i] * (-1 if in_rev else 1) <= 0), True)]]
# V = remove_invalid_efms(N[tuple(m_id2i[m_id] for m_id in inner_m_ids), :], V)
logging.info('Calculated V of shape %s' % 'x'.join((str(it) for it in V.shape)))
efm_id2i = {'efm_%d' % i: i for i in range(0, V.shape[1])}
S = System(N=N, V=V, m_id2i=m_id2i, r_id2i=r_id2i, efm_id2i=efm_id2i, boundary_m_ids=boundary_m_ids)
return S.get_used_system(), efm_id2pws
def test_boundary_metabolites(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 10, 'r3': 10, 'r4': 10, 'r6': 10}], self.r_id2i)
m = get_boundary_metabolites(N, V[:, 0])
result = {(m[self.s_id2i[m_id]], m_id) for m_id in self.s_id2i.keys() if m[self.s_id2i[m_id]]}
self.assertSetEqual({(-1, 'm1_b'), (1, 'm2_b')},
result,
'Was supposed to get -1 m1_b 1 m2_b as boundary metabolites, got %s' % result)
def test_lump_coupled_reactions_NV(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
NV = np.dot(N, V)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
NV_new = np.dot(N_new, V_new)
self.assertListEqual(list(NV.flatten()), list(NV_new.flatten()), 'NV should not have changed')
def test_lump_coupled_reactions_N_2(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
lr_id = next(r_id2lr_id.values())
st = N_new[self.s_id2i['m2_b'], new_r_id2i[lr_id]]
self.assertEqual(1, st, 'The lumped reaction was supposed to produce 1 m2_b, consumes %g instead' % -st)
def test_lump_coupled_reactions_V(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
lr_id = next(r_id2lr_id.values())
r_i = V_new[new_r_id2i[lr_id], :]
self.assertListEqual([10, 1], list(r_i),
'The lumped reaction was supposed to participate in EFMs with coefficients [10, 1] not %s'
% r_i)
def test_yield_no_out_metabolite(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 10, 'r3': 10, 'r4': 10, 'r6': 10}], self.r_id2i)
y = get_yield(N, V[:, 0], out_m_index=self.s_id2i['m3_b'], in_m_index=self.s_id2i['m1_b'])
self.assertEqual(0, y,
'Yield of m3_b with respect to m1_b in EFM 0 was supposed to be 0, got %g instead.' % y)
def test_st_matrix_unrelated_metabolite(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
self.assertEqual(0, N[self.s_id2i['m2'], self.r_id2i['r1']],
'N[m2, r1] was supposed to be -1, got %g instead.' % N[self.s_id2i['m2'], self.r_id2i['r6']])
def test_st_matrix_reactant(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
self.assertEqual(-1, N[self.s_id2i['m1'], self.r_id2i['r2']],
'N[m1, r2] was supposed to be -1, got %g instead.' % N[self.s_id2i['m2'], self.r_id2i['r6']])
def test_st_matrix_product(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
self.assertEqual(1, N[self.s_id2i['m2'], self.r_id2i['r6']],
'N[m2, r6] was supposed to be 1, got %g instead.' % N[self.s_id2i['m2'], self.r_id2i['r6']])
