optimModels.simulation package

Submodules

optimModels.simulation.override_simul_problem module

class optimModels.simulation.override_simul_problem.OverrideKineticSimulProblem(factors={})

Bases: optimModels.simulation.override_simul_problem.OverrideSimulationProblem

This class contains the modifications that will be made to the kinetic model in the simulation process.

Parameters:
  • factors (dict) – Factors to be multiplied with vmax parameter present in the model.
  • simulation ((KO) – factor = 0, under expression: factor > 0 and <1, over expression factor >1)
get_modifications()
set_factors(values)
class optimModels.simulation.override_simul_problem.OverrideSimulationProblem

Bases: object

get_modifications()
simplify_modifications(simulationProblem, objFunction, fitness)
class optimModels.simulation.override_simul_problem.OverrideStoicSimulProblem(constraints={})

Bases: optimModels.simulation.override_simul_problem.OverrideSimulationProblem

This class contains the modifications that will be made to the stoichiometric model in the simulation process.

get_modifications()
simplify_modifications(simulationProblem, objFunction, fitness)

Simplify the constraints to be applied in the simulation. Constraints that not influence the fitness value will be removed.

Parameters:
  • simulationProblem – simulation problem instance
  • objFunction – function to calculate the fitness
  • fitness – reference fitness

optimModels.simulation.run module

optimModels.simulation.run.kinetic_simulation(model, parameters=None, factors=None, time=1000000000.0)

Runs a phenotype simulation using dynamic models. :param model: The kinetic metabolic model. :type model: kineticModel :param parameters: List of parameters that will be set with new values (ex: Dilution, initial concentrations). :type parameters: dict :param factors: Values to by multiplied to the vMax parameters (KO: the value should be 0, Under: value between 0 and 1, :type factors: dict :param Over: value higher than 1) :param time: End time for steady-state. :type time: float

Returns (kineticSimulationResults): The function returns the best solutions found in strain optimization. The kineticSimulationResults have the
flux distribution and metabolites concentration on steady-state, and the modifications made over the original model.

optimModels.simulation.simul_problems module

class optimModels.simulation.simul_problems.GeckoSimulationProblem(model, objective=None, constraints=None, solverId='cplex')

Bases: optimModels.simulation.simul_problems.SimulationProblem

This class contains all required information to perform a simulation of a Gecko metabolic model.

find_essential_proteins()
get_bounds(rId)
get_constraints_reacs()
get_drains()
get_internal_reactions()
get_reactions_ids()
get_uptake_reactions()
set_objective_function(objective)
simulate(overrideSimulProblem=None)

This method preforms the phenotype simulation of the GeckoModel with the modifications present in the overrideSimulProblem. :param overrideProblem: override simulation Problem :type overrideProblem: overrideStoicSimulProblem

Returns:Returns an object with the steady-state flux distribution, protein concentrations and solver status.
Return type:GeckoSimulationResult
class optimModels.simulation.simul_problems.KineticSimulationProblem(model, parameters=None, tSteps=[0, 1000000000.0], timeout=6000, solver='odespy', method=1)

Bases: optimModels.simulation.simul_problems.SimulationProblem

This class contains all required information to perform a simulation of a kinetic metabolic model.

model

kineticModel – Metabolic model object.

parameters

dict (optional) – New values for the parameters present in the model.

t_steps

list – list of exact time steps to evaluate (default: [0,1e9])

timeout

int – Maximum time in secounds allowed to perform the simulation.

get_initial_concentrations()
get_time_steps()
simulate(overrideSimulProblem=None)

This method preform the phenotype simulation of the kinetic model, using the solverId method and applying the modifications present in the instance of overrideSimulProblem.

Parameters:overrideProblem (overrideKineticSimProblem) – Modification over the kinetic model.
Returns:out – Returns an object of type kineticSimulationResult with the steady-state flux distribution and concentrations.
Return type:kineticSimulationResult
class optimModels.simulation.simul_problems.SimulationProblem(model, solverId, method)

Bases: object

Abstract class of simulation problem

get_method()
get_model()
get_solver_id()
simulate(overrideSimulProblem=None)
class optimModels.simulation.simul_problems.StoicSimulationProblem(model, objective=None, minimize=False, constraints=None, solverId='cplex', method='FBA', withCobraPy=False)

Bases: optimModels.simulation.simul_problems.SimulationProblem

This class contains all required information to perform a simulation of a stoichiometric metabolic model.

find_essential_drains()
get_bounds(rId)
get_constraints_reacs()
get_drains()
get_internal_reactions()
get_reactions_ids()
get_uptake_reactions()
set_objective_function(objective)
simulate(overrideSimulProblem=None)

This method preform the phenotype simulation of the stoichiometric model, using the solver method and applying the modifications present in the instance of overrideSimulProblem.

Parameters:overrideProblem (OverrideStoicSimulProblem) – Modification over the stoichiometric model and the default constraints.
Returns:Returns an object with the steady-state flux distribution, solver status, etc..
Return type:StoicSimulationResult

optimModels.simulation.simul_results module

class optimModels.simulation.simul_results.GeckoSimulationResult(modelId, solverStatus, ssFluxesDistrib=None, protConcentrations=None, overrideSimulProblem=None)

Bases: optimModels.simulation.simul_results.SimulationResult

get_protein_concentrations()

Gets the protein concentrations in steady-state {proteinId: concentration value}.

print()
class optimModels.simulation.simul_results.SimulationResult(modelId, solverStatus, ssFluxesDistrib, overrideSimulProblem=None)

Bases: object

Represents the result of a metabolic model simulation at steady-state.

get_fluxes_distribution()

Gets the steady-state flux distribution {reactionId: fluxValue}.

get_override_simul_problem()

Gets the override simulation problem.

get_solver_status()

Returns the solver status result. (see optimModels.utils.constants.solverStatus)

class optimModels.simulation.simul_results.StoicSimulationResult(modelId, solverStatus, ssFluxesDistrib, overrideSimulProblem=None)

Bases: optimModels.simulation.simul_results.SimulationResult

print()
class optimModels.simulation.simul_results.kineticSimulationResult(modelId, solverStatus, ssFluxesDistrib, ssConcentrations=None, overrideSimulProblem=None)

Bases: optimModels.simulation.simul_results.SimulationResult

Represents the result of a dynamic metabolic model simulation on steady-state.

Parameters:
  • modelId (str) – identification of metabolic model
  • solverStatus (int) – simulation result (OPTIMAL = 0, UNKNOWN = 1, ERROR = 2).
  • ssFluxesDistrib (dict) – fluxes distribution achieved in steady state.
  • ssConcentrations (dict) – metabolites concentration in steady state.
  • overrideSimulProblem (overrideKineticSimulProblem) – modifications over the metabolic model.
get_steady_state_concentrations()

Gets the metabolite concentrations in steady-state {metaboliteId: concentration value}.

print()

optimModels.simulation.solvers module

class optimModels.simulation.solvers.odespySolver(solverMethod)

Bases: object

ODE solver method implemented on odespy package.

get_solver(func)

Returns the solver method from odespy package.

Parameters:
  • func – function
  • with ODE system. (function) –

Returns: an instance of odeSolver

Module contents