5. Metabolic control analysis

Further we need to compute the steady state concentrations and fluxes for the steady-state we aim to perform the MCA for. Here we use the concentrations and fluxes from we used for the respective ORACLE parametrization.

from pytfa.io.json import load_json_model
from skimpy.io.yaml import  load_yaml_model
from skimpy.analysis.oracle.load_pytfa_solution import load_concentrations, load_fluxes
from skimpy.core.parameters import ParameterValues
from skimpy.utils.namespace import *

# Units of the parameters are muM and hr
CONCENTRATION_SCALING = 1e6
TIME_SCALING = 1 # 1hr to 1min
DENSITY = 1200 # g/L
GDW_GWW_RATIO = 0.3 # Assumes 70% Water

kmodel =  load_yaml_model('./../../models/varma_strain_1.yml')
tmodel = load_json_model('./../../models/tfa_varma.json')

# Reference steady-state data
ref_solution = pd.read_csv('./../../data/tfa_reference_strains.csv',
                           index_col=0).loc['strain_1',:]

ref_concentrations = load_concentrations(ref_solution, tmodel, kmodel,
                                         concentration_scaling=CONCENTRATION_SCALING)
ref_fluxes = load_fluxes(ref_solution, tmodel, kmodel,
                               density=DENSITY,
                               ratio_gdw_gww=GDW_GWW_RATIO,
                               concentration_scaling=CONCENTRATION_SCALING,
                               time_scaling=TIME_SCALING)

# Extract the current parameter set from the model
parameter_values = {p.symbol:p.value for p in kmodel.parameters.values()}
parameter_values = ParameterValues(parameter_values, kmodel)

With a set of parameters, fluxes and concentration we can then calculate the control coefficients. To perform metabolic control analysis the control-coeffcient expressions need to be compiled by calling kmodel.prepare() and kmodel.compile_mca(parameter_list=parameter_list), where parameter_list is a TabDict contaning the symbols of the parameters we want to the control-coeffcients for. Here we calculate the control-coeffcients with respect to all maximal enzyme rates V_max.

from skimpy.utils.tabdict import TabDict
from skimpy.viz.controll_coefficients import plot_control_coefficients


# Compile mca with parameter elasticities with respect to Vmaxes
parameter_list = TabDict([(k, p.symbol) for k, p in
                          kmodel.parameters.items() if
                          p.name.startswith('vmax_forward')])

kmodel.compile_mca(sim_type=QSSA,ncpu=8, parameter_list=parameter_list)

flux_control_coeff = kmodel.flux_control_fun(ref_fluxes,
                                             ref_concentrations,
                                             [parameter_values, ])

lac_control_coeff = flux_control_coeff.slice_by('sample',0).loc['LDH_D', :]

lac_control_coeff.index = [v.replace('vmax_forward_','')
                            for v in lac_control_coeff.index ]
plot_control_coefficients(lac_control_coeff,
                          filename='lac_control_coeff.html',
                          backend='svg',
                          )