Phenotype Simulation

Stoichiometric Simulation

The phenotype simulation of stoichiometric metabolic models are out of scope of this package. For the phenotype prediction prupose, you can use the available methods on framed package, developed by Daniel Machado.

For more information see: GitHub: https://github.com/cdanielmachado/framed

GECKO Simulation

The phenotype simulation of GECKO metabolic models are out of scope of this package. The GECKO toolbox contains a Python package(geckopy) for enhancing a Genome-scale model to account for Enzyme Constraints, using Kinetics and Omics. ics data.

For more information see: GitHub:https://github.com/SysBioChalmers/GECKO

Kinetic Simulation

optimModels implements some basic support for working with kinetic models.

It now also supports models that contain assignment rules (see for example the Chassagnole 2002 E. coli model).

Wild-type simulation

Running a simple steady state simulation (uses odespy package, LSODA method):

from optimModels import kinetic_simulation

result = kinetic_simulation(model)

result.print()

Simulation with diferent parameters

It is possible override model parameters without changing the model:

result = kinetic_simulation(model, parameters = {'Dil' : 0.2/3600})

result.print()

Knockouts simulation

The simulation of reaction knockouts is done by multiplying vMax parameter with the factor 0, for instance maxG6PDH = 0 will be knockout the reaction vG6PDH:

result = kinetic_simulation(model, factors={'maxG6PDH': 0.0})

result.print()

Under/Over expression simulation

The simulation of under (over) expression enzymes is done by multiplying vMax parameter with the factor less than 1 (higher than 1)

result = kinetic_simulation(model, factors={'maxG6PDH': 2.0})

result.print()