def set_temperature_constraints(model, particle, T):
df, _ = GEMS.format_input(particle)
etc.map_fNT(model, T, df)
etc.map_kcatT(model, T, df)
etc.set_NGAMT(model, T)
etc.set_sigma(model, 0.5)
def split_factor(particle, Ts, sigma):
df, new_params = format_input(particle)
dfparam = etc.calculate_thermal_params(new_params)
results = dict() # results = {'etcYeast7':[]}
# Case (1) No temperature constraints
model = pickle.load(open('../models/aerobic.pkl', 'rb'))
with model as m:
etc.set_sigma(m, sigma)
r = m.optimize().objective_value
results['ori'] = [r for T in Ts]
print('Case 1:', results['ori'])
# Case (2) based on (1) Include the temperature dependent NGAM
model = pickle.load(open('../models/aerobic.pkl', 'rb'))
for T in Ts:
with model as m:
etc.set_NGAMT(m, T)
etc.set_sigma(m, sigma)
r = m.optimize().objective_value
results['NGAM'] = results.get('NGAM', []) + [r]
print('Case 2:', results['NGAM'])
# Case (3) based on (1) Include the temperatuer dependent kcat
model = pickle.load(open('../models/aerobic.pkl', 'rb'))
for T in Ts:
with model as m:
etc.map_kcatT(m, T, dfparam)
etc.set_sigma(m, sigma)
r = m.optimize().objective_value
results['kcat'] = results.get('kcat', []) + [r]
print('Case 3:', results['kcat'])
# Case (4) based on (1) Include the temperature dependent unfolding process
model = pickle.load(open('../models/aerobic.pkl', 'rb'))
for T in Ts:
with model as m:
etc.map_fNT(m, T, dfparam)
etc.set_sigma(m, sigma)
try:
r = m.optimize().objective_value
except:
r = None
results['unfolding'] = results.get('unfolding', []) + [r]
print('Case 4:', results['unfolding'])
# Case (5) with all constraints
model = pickle.load(open('../models/aerobic.pkl', 'rb'))
results['etc'] = etc.simulate_growth(model, Ts, sigma, dfparam)
print('Case 5:', results['etc'])
return results
def simulate_growth(model, Ts, sigma, df, prot):
'''
# model, cobra model
# Ts, a list of temperatures in K
# sigma, enzyme saturation factor
# df, a dataframe containing thermal parameters of enzymes: dHTH, dSTS, dCpu, Topt
# Ensure that Topt is in K. Other parameters are in standard units.
# prot: uniprotid, enzyme to be rescued
'''
rs0 = list()
rs = list()
met1 = model.metabolites.get_by_id('prot_{0}'.format(prot))
old_kcat_coeffs = {
rxn.id: rxn.get_coefficient(met1)
for rxn in met1.reactions
}
met2 = model.metabolites.prot_pool
rxn = model.reactions.get_by_id('draw_prot_{0}'.format(prot))
old_fnt_coeff = rxn.get_coefficient(met2)
for T in Ts:
with model:
# map temperature constraints
etc.map_fNT(model, T, df)
etc.map_kcatT(model, T, df)
etc.set_NGAMT(model, T)
etc.set_sigma(model, sigma)
try:
r = model.optimize().objective_value
except:
print('Failed to solve the problem')
r = 0
print(T - 273.15, r)
rs0.append(r)
# rescue prot
met1 = model.metabolites.get_by_id('prot_{0}'.format(prot))
for rxn_id, old_coeff in old_kcat_coeffs.items():
rxn = model.reactions.get_by_id(rxn_id)
etc.change_rxn_coeff(rxn, met1, old_coeff)
met2 = model.metabolites.prot_pool
rxn = model.reactions.get_by_id('draw_prot_{0}'.format(prot))
etc.change_rxn_coeff(rxn, met2, old_fnt_coeff)
try:
r = model.optimize().objective_value
except:
print('Failed to solve the problem')
r = 0
print(T - 273.15, r)
rs.append(r)
return rs0, rs
def do_fcc_at_T(T,params,sigma,delta):
# Ts: temperatures at which simulation will be performed
# params: a dataframe with Topt, Tm, T90, dCpt of all enzymes
# sigma: enzyme saturation factor
# delta: the fold change to be made for kcat of each enzyme
#
# return a dataframe with temperature as column and enzyme id as index
model = pickle.load(open('../models/aerobic.pkl','rb'))
# 1. Calculate thermal parameters
dfparam = etc.calculate_thermal_params(params)
enzymes = list(set([met.id for met in model.metabolites
if met.id.startswith('prot_') and met.id != 'prot_pool']))
# 2. do fcc at each temperature
data = dict()
etc.map_fNT(model,T,dfparam)
etc.map_kcatT(model,T,dfparam)
etc.set_NGAMT(model,T)
etc.set_sigma(model,sigma)
# Get all enzymes
try: u0 = model.optimize().objective_value
except: u0 = None
print('Model Track u0:',T,u0)
for enz_id in enzymes:
if u0 is None or u0 == 0: data[enz_id.split('_')[1]] = None
else:
with model as m:
enz = m.metabolites.get_by_id(enz_id)
# Perturbe kcat in all reactions of this enzyme
for rxn in enz.reactions:
if rxn.id.startswith('draw_'): continue
new_coeff = rxn.get_coefficient(enz)/(1+delta)
etc.change_rxn_coeff(rxn,enz,new_coeff)
try:u = m.optimize().objective_value
except: u = None
if u is None: fc = None
else: fc = (u-u0)/u0/delta
data[enz.id.split('_')[1]] = fc
# 3. Create a dataframe with temperature as column and enzyme ids as index
lst, index = [], []
for k,v in data.items():
lst.append(v)
index.append(k)
dfres = pd.DataFrame(data={T:lst},index=index)
return dfres
def simulate_a_particle(particle, T):
df, _ = format_input(particle)
sigma = 0.5
# map all temperature constrains on all enzymes
ecmodel = pickle.load(open('../models/aerobic.pkl', 'rb'))
etcmodel = pickle.load(open('../models/aerobic.pkl', 'rb'))
# map temperature constraints
etc.map_fNT(etcmodel, T, df)
etc.map_kcatT(etcmodel, T, df)
etc.set_NGAMT(etcmodel, T)
etc.set_sigma(etcmodel, sigma)
# get enzyme list
enzymes = list(
set([
met.id for met in ecmodel.metabolites
if met.id.startswith('prot_') and met.id != 'prot_pool'
]))
tested_enzymes = []
try:
r = etcmodel.optimize().objective_value
except:
r = 0
rs = [r]
for i in range(len(enzymes) - 1):
fccs = do_a_fcc(etcmodel, tested_enzymes, enzymes)
most_limit_enz_id = find_most_rate_limiting_enzyme(fccs)
tested_enzymes.append(most_limit_enz_id)
if most_limit_enz_id is not None:
remove_temperature_constraints(ecmodel, etcmodel,
most_limit_enz_id.split('_')[1])
try:
r = etcmodel.optimize().objective_value
except:
r = np.nan
else:
r = np.nan
break
print(most_limit_enz_id, r)
rs.append(r)
return rs, tested_enzymes