pyADAPT
Analysis of Dynamic Adaptations in Parameter Trajectories
http://bmi.bmt.tue.nl/sysbio/software/adapt.html
Implementation in Python
Getting Started
Clone this repo, checkout to branch stable from where you would be able to make changes suitable for your own model.
Install pyADAPT to the current python environment: pip install -e .
Define model
Convert sbml file to model definition script: python -m pyADAPT convert -f yourmodel.xml -o yourmodel.py
from yourmodel import YourModel
model = YourModel()
Alternatively you could also define the models directly:
- Inherit from
pyADAPT.basemodel.BaseModel
add_parameter
according to your model in method__init__
- define methods
fluxes
andstate_ode
Define dataset
The dataset is required to be several time-dependent pairs of mean values and standard deviations.
data = pyADAPT.dataset.Dataset(raw_data_path="toyData.mat", data_specs_path="toyData.yaml")
Run the simulation
Analyze parameter 'k1' of the model using 4 processes, ODE solver "LSODA":
from pyADAPT.optimize import optimize
(parameter_trajectories,
state_trajectories,
flux_trajectories,
time) = optimize(model, dataset, 'k1',
n_iter=256,
delta_t=0.2,
odesolver="LSODA",
n_core=4)
Analysis of the parameter trajectories
Please refer to https://research.tue.nl/en/publications/computational-analysis-of-adaptations-during-disease-and-interven