<jupyter_start><jupyter_text>establish the dFBA instance<jupyter_code>from scipy.constants import micro, milli
from pprint import pprint
import pandas
%run ../dfbapy/dfba.py

# define the initial conditions and kinetics data
initial_concentrations = {
    'Acetyl phosphate': 0.8*micro,
    'ADP C10H12N5O10P2': 0.1*milli
}

kinetics_data = {
    'Acetate kinase': {
        'source_1': {
            'Organism': 'Escherichia coli',
            'Parameters': {
                'A': {
                    'unit': 'mM',
                    'chemical': 'ADP C10H12N5O10P2',
                    'value': '1.0'},
                'B': {
                    'unit': 'mM',
                    'chemical': 'Acetyl phosphate',
                    'value': '0.8'}
            },
            'PubMedID': '4362687.0',
            'Publication': 'Janson CA, Cleland WW: The inhibition of acetate, pyruvate, and 3-phosphogylcerate kinases by chromium adenosine triphosphate, J Biol Chem 1974 (249) , 2567-71',
            'SubstitutedRateLaw': '(68.0/milli*A*B)/(50.0*micro*0.34*micro+360.0*micro*B+0.34*micro*A+A*B)',
            'Temperature': '25.0',
            'pH': '7.0',
            'SabioReactionID': 71,
        }
    }
}

# pprint(kinetics_data)

# define inital conditions
total_minutes = 200
timestep = 50
temp_c = 25
ph = 7<jupyter_output><empty_output><jupyter_text>Standard FBA execution<jupyter_code>%run ../dfbapy/dfba.py
dfba = dFBA('e_coli_core.xml', kinetics_data = None, verbose = False, printing = True, jupyter = True)
dfba.simulate(total_minutes, timestep)
dfba.export('simple_fba')<jupyter_output>objective value:  0.8739215069684301<jupyter_text>Implementation of kinetics_data<jupyter_code>%run ../dfbapy/dfba.py
dfba = dFBA('e_coli_core.xml', kinetics_data = kinetics_data, verbose = False, printing = True, jupyter = True)
dfba.simulate(total_minutes, timestep)
dfba.export('dfba')<jupyter_output>timestep 1
Acetate kinase flux: 0.0
fluxes: [0.0]



objective value for timestep 1:  0.8739215069684301
timestep 2
Acetate kinase flux: 0.0
fluxes: [-0.0]



objective value for timestep 2:  0.8739215069684301
timestep 3
Acetate kinase flux: 0.0
fluxes: [-0.0]



objective value for timestep 3:  0.8739215069684301
timestep 4
Acetate kinase flux: 0.0
fluxes: [-0.0]



objective value for timestep 4:  0.8739215069684301<jupyter_text>Implementation of initial_concentrations<jupyter_code>%run ../dfbapy/dfba.py
dfba = dFBA('e_coli_core.xml', kinetics_data = kinetics_data, verbose = False, printing = True, jupyter = True)
dfba.simulate(total_minutes, timestep, initial_concentrations)
dfba.export('dfba-initial_conc')<jupyter_output>timestep 1
Acetate_kinase_kinetics: 0.3140500447983152 <= 1.0*ACKr - 1.0*ACKr_reverse_b49c0 <= 0.3140500447983152
Acetate kinase flux: 0.3140500447983152



objective value for timestep 1:  0.314050044798317
timestep 2
Acetate_kinase_kinetics: 8617.864752157508 <= 1.0*ACKr - 1.0*ACKr_reverse_b49c0 <= 8617.864752157508
Acetate kinase flux: 8617.864752157508



objective value for timestep 2:  8617.864752157508
timestep 3
Acetate_kinase_kinetics: 67982.94451648787 <= 1.0*ACKr - 1.0*ACKr_reverse_b49c0 <= 67982.94451648787
Acetate kinase flux: 67982.94451648787



objective value for timestep 3:  67982.94451648787
timestep 4
Acetate_kinase_kinetics: 67997.83748635459 <= 1.0*ACKr - 1.0*ACKr_reverse_b49c0 <= 67997.83748635459
Acetate kinase flux: 67997.83748635459



objective value for timestep 4:  67997.83748635459
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