This algorithm is able to integrate gene regulatory and metabolic networks by using conditional probabilities to represent the constraints imposed by regulation. PROM can predict an organism's phenotype after the knockout of a regulator.
This Python Implementation is based on the publication:
Chandrasekaran, S., & Price, N. D. (2010). Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proceedings of the National Academy of Sciences of the United States of America, 107(41), 17845–17850. https://doi.org/10.1073/pnas.1005139107
The directory 'original' contains different Implementations of the algorithm in Matlab.
The jupyter notebook 'PROM M.tuberculosis Tutorial.ipynb' explains how to use the PROM algorithm.
'PROM_v3.py' contains the Python Implementation of PROM.