src.model_balancing.model_balancing_noncvx

A module for performing full model balancing (using SciPy).

Module Contents

class src.model_balancing.model_balancing_noncvx.ModelBalancing(S: numpy.array, fluxes: numpy.array, A_act: numpy.array, A_inh: numpy.array, metabolite_names: List[str], reaction_names: List[str], state_names: List[str], rate_law: str = 'CM', alpha: float = 1.0, beta: float = 0.0, **kwargs)

Bases: object

A class for performing Model Balancing (non-convex version).

This version of model balancing solves the exact non-convex problem. The α parameter can be used to tune between a convex version (α = 0) and the full version (α = 1).

S

fluxes

A_act

A_inh

metabolite_names

reaction_names

state_names

rate_law = 'CM'

alpha = 1.0

beta = 0.0

Ncond

ln_geom_mean

precision_ln

ln_lower_bound

ln_upper_bound

ln_true_value

var_dict_to_vector(var_dict: Dict[str, numpy.array], param_order: List[str] = INDEPENDENT_VARIABLES) → numpy.array

Get the variable vector (x).

extend_var_dict_to_dependent_params(var_dict: Dict[str, numpy.ndarray]) → None

var_vector_to_dict_all(x: numpy.ndarray) → Dict[str, numpy.ndarray]

Get a dictionary with all dependent and independent variable values.

var_vector_to_z_scores(x: numpy.ndarray) → Dict[str, float]

var_dict_to_z_scores(var_dict: Dict[str, float]) → Dict[str, float]

objective_function(x: numpy.ndarray) → float

Calculate the sum of squares of all Z-scores for a given point (x).

The input (x) is a stacked version of all the independent variables, assuming the following order: Km, Ka, Ki, Keq, kcatf, conc_met By default, if x is None, the objective value for (the exponent of) self.x is returned.

penalty_term_beta(var_dict: Dict[str, float]) → float

Calculate the penalty term for c/Km.

property driving_forces: numpy.ndarray

Calculates the driving forces of all reactions.

property ln_kcatr: numpy.ndarray

Calculate the kcat-reverse based on Haldane relationship constraint.

property ln_eta_thermodynamic: numpy.ndarray

Calculate the thermodynamic term of the enzyme.

property ln_eta_kinetic: numpy.ndarray

Calculate the kinetic (saturation) term of the enzyme.

property ln_eta_regulation: numpy.ndarray

Calculate the regulation (allosteric) term of the enzyme.

property ln_conc_enz: numpy.ndarray

Calculate the required enzyme levels based on fluxes and rate laws.

is_gmean_feasible() → bool

Check if the gmean is a thermodynamically feasible solution.

This is useful because we sometimes would like to initialize the optimization with the geometric means, but that can only be done if that point is feasible (otherwise, the dependent parameter conc_enz is not defined).

property thermodynamic_constraints: scipy.optimize.LinearConstraint

Construct the thermodynamic constraints for the variable vector.

extend_variable_vector(x: numpy.ndarray) → numpy.ndarray

Add the dependent variables to a vector of the independen ones.

property parameter_constraints: scipy.optimize.NonlinearConstraint

initialize_with_gmeans() → None

Initialize the independent parameters with their gmeans.

Note that the dependent parameters (kcatr and ln_conc_enz) can both be very far from their gmeans, and that the system might not be thermodynamically feasible.

solve(basinhopping_kwargs: dict | None = None, minimizer_kwargs: dict | None = None) → scipy.optimize.OptimizeResult

Find a local minimum of the objective function using SciPy.

get_z_scores() → Dict[str, numpy.array]

print_z_scores() → None

Print the z-score values for all variables.

print_status() → None

Print a status report based on the current solution.

property objective_value: float

Get the objective value (i.e. the sum of squares of all z-scores).

to_state_sbtab() → sbtab.SBtab.SBtabDocument

Create a state SBtab.

The state SBtab contains the values of the state-dependent variables, i.e. flux, concentrations of metabolites, concentrations of enzymes, and the ΔG’ values.

to_model_sbtab() → sbtab.SBtab.SBtabDocument

Create a model SBtab.

The model SBtab contains the values of the state-independent variables, i.e. kcatf, kcatr, Km, Ka, and Ki.