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Computational
Systems Biology Approaches to Understanding the Arabidopsis Hypersensitive
Response |
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Major Updates In Progress
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Summary
Motivation
Cross-talk and feedback regulation in signal transduction networks are often quite complex. Mathematical modeling was used to help understand the Arabidopsis hypersensitive response (HR) to avirulent Pseudomonas syringae. The goal was to simulate the time course progression of the most important components of the response in silico. These predictions were then compared to experimental data for validation. If we truly understand the response, we should be able to get agreement with the data even for data the model has never seen.
Strategy
The known measurable components of the response were taken as model variables. These included death of individual cells (PCD), salicylic acid (SA) and reactive oxygen (ROS) accumulation, and level of apoplastic superoxide dismutase (SOD). Other variables were included if predicted to be critical to system dynamics. Initial estimates of kinetic parameters and time delays were made from experimental data and subsequently refined by global fitting of simulated to experimental data. This process resulted in a system of ten delay differential equations governed by expert-system type rules. This system was solved numerically using engineering software (MATLAB). A one-to-one correspondence was maintained between model variables and specific signaling components. The mathematical forms for relationships between model variables also corresponded one-to-one with experimentally observed relationships between signaling components. As such, the assumptions questioned by new data can be readily identified.
Results
In silico simulations of the time course of changes in levels of salicylic acid, PCD and hydrogen peroxide match experimental data. We have used the model to prove that direct negative autoregulation of salicylic acid biosynthesis does not exist in this system. Including terms for this extra negative feedback loop made it impossible for simulated data to match experimental results. Simulations also determined that NPR1-dependent negative feedback on PCD can not affect the fraction of total PCD seen late in HR progression as a direct consequence of high levels of superoxide. The dynamic profiles of apoplastic superoxide dismutase (SOD) activity and two putative gene induction events have been predicted. “Sensitivity” analysis has been used to predict which model components have the most significant influence on overall system dynamics. These predictions will aid in design of further experiments to test our knowledge of control of the HR.