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()