The idea is to model the Fatty Acid elongation pathway(from KEGG: hsa00062) using Stochastic Petri Nets(described formally for example here and here).
The pathway, as obtained from KEGG, was first manually converted to a Stochastic Petri Net using SNOOPY for a first experimentation. The net was then simplified to capture only the most important aspects of the pathway. SNOOPY files for both the initial and simplified nets are in the /model directory.
While SNOOPY is good for a first exploration it is not very convenient for performing multiple simulations(for example for the inference of the SPN). fa.py contains functions to read the model from PySCeS format(located in the /model directory along with the SNOOPY files) into the appropriate data structures and then simulate it.
infer.py contains a function to read the real data(real FA outputs of the pathway), which are located in the /data directory, and score a particular SPN based on how similar are the simulated data from that SPN to the real data.