Behaviour Prediction of the CagA Pathway in Gastric Cancer using Petri nets

Abstract

Cytotoxin associated gene A (CagA), is the only virulence factor of Heli cobacter pylori that is translocated inside gastric epithelial cell responsible for the induction of infection leading to gastric cancer. Stochastic Petri net is a well known method for modelling of the signalling pathways which is used to model and analyze the pathological process of CagA induced in gastric cancer. The Petri net model of CagA was used to make predictions about behaviours associated with physiological or pathological responses, preceded by the validation of the model in a stepwise manner. This study provides insights into the dynamical behaviour of the infectious pathway of CagA from hijacking the host machinery, getting attached to host proteins (SHP2, CRK, GRB2, and PI3K) and to initiate the infection that ultimately leads to disturbance of cellular processes (cell scattering, actin reorganization, cell proliferation and inflammation). Studies suggested that several key proteins including ERK and NF-κB are responsible for induction of these cellular re sponses. Our simulation results of the activity levels of all of the suggested proteins and the key proteins are in good agreement with the previously re ported western blot experiments in support to the predicted behaviour of our model. Our results revealed that inhibition of CagA by Gastrokine 1 (gastric mucosal protein) suppressed the carcinoma by not only controlling the relative level of SHP2, PI3K proteins but also CRK and GRB2. Our v vi results suggest that Gastroine1 can be an attractive remedial target in the CagA associated pathway for the therapeutic intervention against Helicobac ter pylori infection and the delineated methodology using Stochastic Petri net is a promising method to develop, analyze and predict the behaviour of other signalling pathways in biological system.