1. Quick start

In this quick start tutorial we introduce the main functions ODE to build and manipulate SKiMpy models using oversimplfied model containing one reaction.

All main modelling functionality is imported from submodules of ‘skimpy.core’ and the libary of mechanisms is imported from ‘skimpy.mechanisms’. The model is initialized by the ‘KineticModel’ constructor.

# Load core functionalities + numpy (because useful)
import numpy as np
from skimpy.core import *
from skimpy.mechanisms import *

 # Create a model
this_model = KineticModel()

Reaction objects are constricted from a name, a mechanism object and a reactants set inherited from the respective mechanism object to ensure the consitency of e.g. order kinetic mechanisms.

# Name the reaction
name = 'pfk'

# Define reaction reactants sets
metabolites = ReversibleMichaelisMenten.Reactants(substrate = 'A',
                                                   product = 'B')

# Create the reaction object
pfk = Reaction(name=name,
               mechanism = ReversibleMichaelisMenten,
               reactants=metabolites,
               )

# Add the reaction
this_model.add_reaction(pfk)

Each mechanism has respective Parameter “ItemSets’ that defines the respective parameters for the mechanism. Mechanism specific parameters set can be created using the inherited Mechanism.Parameters constructor, and can then be assigned using the model.parametrize_by_reaction() method:

# Define reaction parameters
parameters = ReversibleMichaelisMenten.Parameters(
    vmax_forward = 1.0,
    k_equilibrium=2.0,
    km_substrate = 10.0,
    km_product = 10.0,
    total_enzyme_concentration = 1.0,
)

# Assing the parameters to the model
this_model.parametrize_by_reaction({pfk.name:parameters})

Alternatively parameters can be directly assinged using the model.parameter poperty:

# Direct assginment of parameter values
this_model.parameters.vmax_forward_pfk.value = 2.0

In this way simple models can be constructed. To integrate the ordinary differntial equations we first compile the respective functions using CYTHON by calling the KineticModel.compile_ode() method. Then the initial value problem can be initialized and the model can be solved using the KineticModel.solve_ode() method. Additionally, the solution object has standardized ploting methods implemented (Note that an output folde needs to be generated manually).

# Compile the symbolic expressions of the ODE system
this_model.compile_ode()

# Assing initial conditions
this_model.initial_conditions['A'] = 1.0
this_model.initial_conditions['B'] = 1.0

# solve the ODEs
this_sol_qssa = this_model.solve_ode(np.linspace(0.0, 100.0, 1000), solver_type='cvode')

this_sol_qssa.plot('output/uni_uni_base_out_qssa.html')