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 format_input(thermalParams):
new_params = params.copy()
for key,val in thermalParams.items():
[ind,col] = key.split('_')
new_params.loc[ind,col] = val
# Update T90
new_params['T90'] = params['T90']-params['Tm'] + new_params['Tm']
df = etc.calculate_thermal_params(new_params)
return df,new_params
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 format_input(thermalParams):
# thermalParams: a dictionary with ids like uniprotid_Topt
params = pd.read_csv('../data/model_enzyme_params.csv', index_col=0)
new_params = params.copy()
for key, val in thermalParams.items():
[ind, col] = key.split('_')
new_params.loc[ind, col] = val
# Update T90
new_params['T90'] = params['T90'] - params['Tm'] + new_params['Tm']
df = etc.calculate_thermal_params(new_params)
return df, new_params