Modeling the dynamics of the plant immune response

Abstract

Dynamic modeling is essential for understanding the temporal behavior of a system. Deriving dynamic models from biological omics data can enable effective information reduction by leveraging a few interpretable parameters and capturing the hidden structure in the data. Thanks to the availability of RNA-seq, temporal transcriptomes have been widely profiled as dynamic snapshots of biological responses. My PhD study focuses on dynamic modeling of plant immunity, a plant defense response induced by pathogens. There are two well-defined modes of inducible immunity of plant to overcome pathogen attack, namely pattern triggered immunity (PTI) and effector triggered immunity (ETI). Researchers have generated rich sources of temporal transcriptome data in plants upon challenge of pathogens or pathogen derivatives during both PTI and ETI. My contribution to dynamic modeling of plant immunity comes primarily with two projects. In my main project, I developed a novel computational approach based on an ordinary differential equation system to interpreting the transcriptome dynamics during ETI. The modeling results uncovered intrigue data patterns that direct deep insights into the transcriptional regulation of transcription factors during ETI. In my other project, I developed mechanistic models based on the transcript response of CBP60g, a marker gene of pattern-triggered immunity. The model not only interpreted the dynamics of CBP60g response but also predicted the mechanistic roles of three plant immunity genes in regulating CBP60g transcription. Overall, my efforts on dynamic modeling of plant immunity bring novel mathematical frameworks for transcript/transcriptome data interpretation and derive valuable biological predictions that shed light on transcriptional mechanisms of plant immunity.