GeKiM (Generalized Kinetic Modeler) is a Python package designed for creating, interpreting, and modeling arbitrary kinetic schemes. Schemes are defined by the user in a dictionary of species and transitions, which is used to initialize an instance of the NState class. Choose (or make) and initialize a simulator for the instance and run it. Field-specific practices are found in gekim/fields.
With pip:
pip install gekimOr directly from the source code (recommended):
git clone https://github.com/kghaby/GeKiM.git
cd GeKiM
pip install .Here is a basic example of how to use GeKiM to create and simulate a kinetic system:
import gekim as gk
from gekim.fields.bio.enzyme.inhib import irrev as ii
# Define your kinetic scheme in a configuration dictionary
concI0,concE0 = 100,1
scheme = {
'species': {
"I": {"y0": concI0, "label": "I"},
"E": {"y0": concE0, "label": "E"},
"EI": {"y0": 0, "label": "EI"},
},
'transitions': {
"kon": {"k": 0.01, "source": ["2E","I"], "target": ["EI"]},
"koff": {"k": 0.1, "source": ["EI"], "target": ["2E","I"]},
}
}
# Initialize a system with your schematic dictionary
system = gk.schemes.NState(scheme,quiet=False)
# Choose a simulator and go. In this example we're doing a deterministic
# simulation of the concentrations of each species over time.
# Note that `system.simulator() = gk.simulators.ODESolver(system)` may be more doc-hint friendly
system.set_simulator(gk.simulators.ODESolver)
system.simulator.simulate()
# Fit the data to experimental models to extract mock-experimental measurements
final_state = system.species["EI"].simout["y"]
all_bound = system.sum_species_simout(blacklist=["E","I"])
fit_output = ii.kobs_uplim_fit_to_occ_final_wrt_t(
t,final_state,nondefault_params={"Etot":{"value":concE0,"vary":False}})For more detailed examples, please refer to the examples directory.
Documentation and example notebook(s) are pending.
If you have suggestions or want to contribute code, please feel free to open an issue or a pull request.
GeKiM is licensed under the GPL-3.0.