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

Area of Science:

• Synthetic biology
• Plant pathology
• Computational biology

Background:

• Synthetic biology advances enable genetic feedback control circuits for plant resilience against pathogens.
• Accurate dynamical models are crucial for designing effective genetic control circuits in pathogen-infected plants.

Purpose of the Study:

• To develop and compare four dynamical models (linear, Hill Function, standard S-System, extended S-System) for pathogen-infected plant gene regulatory networks (GRNs).
• To assess model viability based on biological complexity, accuracy, gene regulation identification, predictive capability, AIC, and robustness to parameter uncertainty.

Main Methods:

• Data-driven modeling approach to develop and compare four distinct dynamical models.
• Assessment of models using criteria including gene regulation identification, predictive capability, Akaike Information Criterion (AIC), and parameter uncertainty robustness.
• A defined ranking score was used to evaluate and compare the models.

Main Results:

• The Hill Function model ranked lowest, while the extended S-System model ranked highest in the overall comparison.
• The linear model demonstrated consistent performance across all comparison criteria.
• The extended S-System model shows promise for balancing biological complexity and accuracy in GRN modeling.

Conclusions:

• The linear model is a preferred choice for data-driven modeling of this specific pathogen-infected plant GRN due to its consistent performance.
• While the extended S-System model offers high overall performance, the linear model provides a reliable alternative.
• The study provides valuable insights into selecting appropriate models for plant GRN dynamics in the context of synthetic biology and plant defense.