A dynamic mathematical model to clarify signaling circuitry underlying programmed cell death control in Arabidopsis...

Plant cells undergo programmed cell death in response to invading pathogens. This cell death limits the spread of the infection and triggers whole plant antimicrobial and immune responses. The signaling network connecting molecular recognition of pathogens to these responses is a prime target for manipulation in genetic engineering strategies designed to improve crop plant disease resistance. Moreover, as alterations to metabolism can be misinterpreted as pathogen infection, successful plant metabolic engineering will ultimately depend on controlling these signaling pathways to avoid inadvertent activation of cell death. Programmed cell death resulting from infection of Arabidopsis thaliana with Pseudomonas syringae bacterial pathogens was chosen as a model system. Signaling circuitry hypotheses in this model system were tested by construction of a differential-equations-based mathematical model. Model-based simulations of time evolution of signaling components matched experimental measurements of programmed cell death and associated signaling components obtained in a companion study. Simulation of systems-level consequences of mutations used in laboratory studies led to two major improvements in understanding of signaling circuitry: (1) Simulations supported experimental evidence that a negative feedback loop in salicylic acid biosynthesis postulated by others does not exist. (2) Simulations showed that a second negative regulatory circuit for which there was strong experimental support did not affect one of two pathways leading to programmed cell death. Simulations also generated testable predictions to guide future experiments. Additional testable hypotheses were generated by results of individually varying each model parameter over 2 orders of magnitude that predicted biologically important changes to system dynamics. These predictions will be tested in future laboratory studies designed to further elucidate the signaling network control structure.