Plant Microbe Interaction-Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network Modeling

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant-pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant-microbe interactions. These methods will aid researchers in understanding plant-pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.