Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: a comparative analysis

Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten, standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of modelling the pathogen-infected plant GRN. Using our defined ranking score, our analyses show that while the extended S-System model ranks highest in the overall comparison, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN.