src.model_balancing.model_balancing_noncvx
==========================================

.. py:module:: src.model_balancing.model_balancing_noncvx

.. autoapi-nested-parse::

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





Module Contents
---------------

.. py:class:: 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: :py:obj:`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).


   .. py:attribute:: S


   .. py:attribute:: fluxes


   .. py:attribute:: A_act


   .. py:attribute:: A_inh


   .. py:attribute:: metabolite_names


   .. py:attribute:: reaction_names


   .. py:attribute:: state_names


   .. py:attribute:: rate_law
      :value: 'CM'



   .. py:attribute:: alpha
      :value: 1.0



   .. py:attribute:: beta
      :value: 0.0



   .. py:attribute:: Ncond


   .. py:attribute:: ln_geom_mean


   .. py:attribute:: precision_ln


   .. py:attribute:: ln_lower_bound


   .. py:attribute:: ln_upper_bound


   .. py:attribute:: ln_true_value


   .. py:method:: var_dict_to_vector(var_dict: Dict[str, numpy.array], param_order: List[str] = INDEPENDENT_VARIABLES) -> numpy.array

      Get the variable vector (x).



   .. py:method:: extend_var_dict_to_dependent_params(var_dict: Dict[str, numpy.ndarray]) -> None


   .. py:method:: var_vector_to_dict_all(x: numpy.ndarray) -> Dict[str, numpy.ndarray]

      Get a dictionary with all dependent and independent variable values.



   .. py:method:: var_vector_to_z_scores(x: numpy.ndarray) -> Dict[str, float]


   .. py:method:: var_dict_to_z_scores(var_dict: Dict[str, float]) -> Dict[str, float]


   .. py:method:: 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.



   .. py:method:: penalty_term_beta(var_dict: Dict[str, float]) -> float

      Calculate the penalty term for c/Km.



   .. py:property:: driving_forces
      :type: numpy.ndarray


      Calculates the driving forces of all reactions.


   .. py:property:: ln_kcatr
      :type: numpy.ndarray


      Calculate the kcat-reverse based on Haldane relationship constraint.


   .. py:property:: ln_eta_thermodynamic
      :type: numpy.ndarray


      Calculate the thermodynamic term of the enzyme.


   .. py:property:: ln_eta_kinetic
      :type: numpy.ndarray


      Calculate the kinetic (saturation) term of the enzyme.


   .. py:property:: ln_eta_regulation
      :type: numpy.ndarray


      Calculate the regulation (allosteric) term of the enzyme.


   .. py:property:: ln_conc_enz
      :type: numpy.ndarray


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


   .. py:method:: 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).



   .. py:property:: thermodynamic_constraints
      :type: scipy.optimize.LinearConstraint


      Construct the thermodynamic constraints for the variable vector.


   .. py:method:: extend_variable_vector(x: numpy.ndarray) -> numpy.ndarray

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



   .. py:property:: parameter_constraints
      :type: scipy.optimize.NonlinearConstraint



   .. py:method:: 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.



   .. py:method:: solve(basinhopping_kwargs: Optional[dict] = None, minimizer_kwargs: Optional[dict] = None) -> scipy.optimize.OptimizeResult

      Find a local minimum of the objective function using SciPy.



   .. py:method:: get_z_scores() -> Dict[str, numpy.array]


   .. py:method:: print_z_scores() -> None

      Print the z-score values for all variables.



   .. py:method:: print_status() -> None

      Print a status report based on the current solution.



   .. py:property:: objective_value
      :type: float


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


   .. py:method:: 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.



   .. py:method:: 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.