Modeling the Plant Immune Signaling Network

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

Plant disease is a major cause of crop loss every year. Plants have developed a complex immune signaling network to counter attacks by disease-causing microbes. This project will develop computer models of the plant immune system based on genes expressed in response to pathogen challenge in the model plant, Arabidopsis thaliana. This information will be used to develop experimental and theoretical approaches suitable for understanding this network. These computer models have the advantage of allowing rapid exploration of the plant's immune system response under various conditions which are not practical to investigate by actual biological experimentation. In this project post-doctoral researchers and graduate students will obtain interdisciplinary training in systems biology and computer modeling. Part of this research will be driven by data generated by teams of undergraduate students in a program that engages biology undergraduate students who typically do not participate in science research. A protocol to extend this club-style, research-driven biology education platform to other institutions will be established in collaboration with a primarily undergraduate institution. The plant immune signaling network is a large, complex network with unusual emergent properties. The network is under constant attack by diverse pathogens that evolve much faster than plants. A plant, unlike an animal, does not have an adaptive immune system, and it must rely on the emergent network properties of its immune signaling network to successfully defend itself. In this project, computer models of the pathogen-triggered immune signaling network will be expanded to facilitate identification of emergent properties of the immune system. Modeling capabilities will be expanded on two fronts. In Aim 1 a fully dynamic differential equation-based model of pathogen triggered immunity will be built and tested. This model will be used for large-scale in silico experiments to identify potential immune engineering targets. In Aim 2 the pathogen triggered immunity model will be extended to explain spatially heterogeneous situations, which commonly occur in nature. To support this modeling effort, data will be collected from leaves with spatial genetic variation generated by laser heat-inducible recombination. The model will be used to predict cell-to-cell signal flows, such as those mediated by the diffusible primary immune hormones salicylate and jasmonate, and the model predictions will be experimentally tested. This award is supported jointly by programs in Systems and Synthetic Biology (Division of Molecular and Cellular Biosciences) and Symbiosis, Defense and Self-Recognition (Division of Integrative Organismal Systems).