def test_attach_regulation(self, random_model):
random_microbe = Microbe(random_model)
random_microbe.attach_regulation(Regulator)
reg = random_microbe.regulator
rxns = random_microbe.ex_reactions
mets = random_microbe.ex_metabolites
assert len(reg.state_space) == len(rxns) + len(mets)
assert len(reg.action_space) == 2 * len(rxns)
def test_death(self, core_model):
microbe = Microbe(core_model)
glc_rxn = microbe.get_rxn_byid('EX_glc__D_e')
glc_rxn.fix_rxn_bound(1.0, 'fixed') # because fix tolerance is +-1
obj_val, status, penalty = microbe.optimize()
assert np.isclose(obj_val, 0.0, rtol=1e-2)
assert status == 'failed'
assert penalty == Microbe.death_penalty
def test_optimization(self, core_model):
microbe = Microbe(core_model)
obj_val, status, death_penalty = microbe.growth
assert np.isclose(obj_val, 0.874, rtol=1e-2)
assert status == 'optimal'
assert np.isclose(death_penalty, 0.2)
obj_val1, status1, death_penalty1 = microbe.optimize(
opt_type='default')
assert obj_val == obj_val1
assert status == status1
assert death_penalty == death_penalty1
def test_initialization(self, random_model):
random_microbe = Microbe(random_model, init_biomass=1.0)
assert random_microbe._model == random_model
assert random_microbe.id == random_model.id
assert random_microbe.abundance == 1.0
assert len(random_microbe.metabolites) == len(random_model.metabolites)
assert len(random_microbe.reactions) == len(random_model.reactions)
def check_solver(self, random_model):
random_microbe = Microbe(random_model)
solvers = optlang.list_available_solvers()
assert any(self.solvers.values) # any solver available
if solvers['GUORBI']:
assert random_microbe.solver == 'gurobi'
if solvers['GLPK']:
assert random_microbe.solver == 'glpk'
def core_regulator_inst(core_model):
core_microbe = Microbe(core_model)
rxn_list = [rxn.id for rxn in core_microbe.ex_reactions]
comp_list = [met.id for met in core_microbe.ex_metabolites]
return core_microbe, Regulator(rxn_list, comp_list)
def random_regulator_inst(random_model):
random_microbe = Microbe(random_model)
rxn_list = [rxn.id for rxn in random_microbe.ex_reactions]
comp_list = [met.id for met in random_microbe.ex_metabolites]
return random_microbe, Regulator(rxn_list, comp_list)
def random_microbiome(random_microbes):
return [Microbe(m) for m in random_microbes]
def known_microbiome(known_microbes):
return [Microbe(m) for m in known_microbes]
def calc_reward(self, core_model):
core_microbe = Microbe(core_model, init_biomass=1.0)
reward = core_microbe.calc_reward(-0.1)
a = Microbe.alpha
b = Microbe.beta
assert np.isclose(reward, (a * 0.874 + b * -0.1), rtol=1e-2)
def test_update_abundance(self, core_model):
core_microbe = Microbe(core_model, init_biomass=1.0)
core_microbe.optimize()
core_microbe.update_abundance(dt=0.1)
assert np.isclose(core_microbe.abundance, (1.0 + 1.0 * 0.874 * 0.1),
rtol=1e-2)
def test_get_metbyid(self, core_model):
microbe = Microbe(core_model)
glc_met = microbe.get_met_byid('glc__D_e')
assert glc_met.id == 'glc__D_e'
assert glc_met._met.id == 'glc__D_e'
def test_get_rxn_byid(self, core_model):
microbe = Microbe(core_model)
glc_rxn = microbe.get_rxn_byid('EX_glc__D_e')
assert glc_rxn.id == 'EX_glc__D_e'
assert glc_rxn._rxn.id == 'EX_glc__D_e'
def test_ex_reactions_and_fluxes(self, random_model):
microbe = Microbe(random_model)
ex_reactions = microbe.ex_reactions
assert len(microbe.ex_fluxes) == len(ex_reactions)
def test_eq_hash(self, random_model):
model1 = Microbe(random_model, init_biomass=1.0)
model2 = Microbe(random_model, init_biomass=5.0)
assert model1 == model2
assert len(set([model1, model2])) == 1