Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: A comparative analysis. (September 2022)

• Record Type: Journal Article
• Authors: Foo, Mathias; Dony, Leander; He, Fei
• Is Part Of: Bio systems. Volume 219(2022)
• Publication Date: 2022-09
• Subjects: Data-driven modelling -- Gene regulatory network -- Linear model -- Hill function model -- S-System model -- Synthetic biology; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2022.104732
• Languages: English
• ISSNs: 0303-2647

Abstract

Abstract: 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 with Hill coefficient (Hill Function), 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 balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach. Graphical abstract: Image 1 Highlights: A comparative analysis of developed data-driven models is presented for a pathogen-infected plant gene regulatory network. Four different commonly used gene regulatory network models are compared in this study according to four criteria. Our analysis shows that the linear and extended S-System models are viable in balancing biological complexity and accuracy.