Biochemical pathway robustness prediction with graph neural networks

The robustness property of a biochemical pathway refers to maintaining stable levels of molecular concentration against the perturbation of parameters governing the underlying chemical reactions. Its computation requires an expensive integration in parameter space. We present a novel application of Graph Neural Networks (GNN) to predict robustness indicators on pathways represented as Petri nets, without the need of performing costly simulations. Our assumption is that pathway structure alone is sufficient to be effective in this task. We show experimentally for the first time that this is indeed possible to a good extent, and investigate how different architectural choices influence performances.