LumpingPositiviser

This repository contains the implementation of the algorithm designed in the paper Interpretable exact linear reductions via positivity by Gleb Pogudin and Xingjian Zhang. The code takes as input an ODE model and a set of observables, performs exact linear reduction using CLUE, and then performs re-parametrization of the reduction aiming at improving the interpretability of the model.

How to use:

The input ODE model and observables should be stored in *.ode and *.obs files, respectively.

The format of *.ode files

The detailed description of the format can be found here. Briefly, one can use the format to write either an ODE system directly or to specify a chemical reaction network.

An example of defining an ODE system directly:

begin model ExampleODE
  begin ODE
    d(x) = x + y
    d(y) = x^2 - 3 * y
  end ODE
end model

An example of defining an ODE system as CRN:

begin model ExampleCRN
  begin reactions
    A + B -> C, k1
    B + 2 * C -> A, k2
  end reactions
end model

The format of *.obs files

An *.obs file contains the observables (linear combinations of the variables of the system) one per line.

How to run

python3 run.py model.ode model.obs [path_to_julia_binary]

If the path to the julia binary is not given, a shell command julia will be used. As the result of the computation, there will be two files model_final_macrovariables.txt and model_final_ode.txt containing the new variables and the reduced ODE system, respectively.