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 and state_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