Amy Marshall-Colon is an associate professor in the Department of Plant Biology at the University of Illinois Urbana-Champaign. The focus of her research is to explore the regulatory mechanisms controlling nitrogen uptake and assimilation in plants using a systems biology approach. The overarching goal of her research is to use predictive network modeling to identify the most effective engineering strategies to improve crop productivity in response to environmental challenges imposed by global climate change.
Specific research interests include dynamic network modeling to explore regulation of long-distance nitrogen signaling between roots and shoots; using multi-scale modeling to integrate new and legacy plant models across biological levels for more accurate prediction of plant response to environmental signals; and exploring molecular networks that underlie high- and low-quality legume-rhizobium mutualisms.
Current crop models predict an increasing gap between food supply and demand over the next 50 years. Technology is needed to predict the fitness of various crops in response to climate and resource availability, and aid in the design of crop ideotypes. I will highlight our efforts to generate virtual plant models that capture whole system dynamics in response to in silico environmental and genetic perturbations, using the yggdrasil computational framework.
This modeling platform was used to integrate models of gene expression, photosynthetic metabolism, and leaf physiology to evaluate the effect of photosynthesis and transpiration under various environmental conditions, and combine modeling and advanced visualization approaches to make direct observations about changes in plant structure, biomass, and yield in response to environmental perturbations. Scientific outcomes of these efforts include an improved prediction accuracy for soybean photosynthesis rate in the context of perturbed atmospheric CO2 levels, and refined canopy-level photosynthesis rate predictions due to a more accurate simulation of leaf area and leaf angle using 3D visualization tools. Additionally, the technical developments in support of these goals have included new mechanisms for intra- and inter-disciplinary model communication, visualization, and simulation ensemble construction.
The improved accuracy of model predictions and the realistic rendering of model-simulated plants is an important step toward the in silico “testing” of ideotype designs under different environmental conditions, whereby dozens of observations about ideotype performance under varying scenarios can be made by researchers. In silico exploration has the potential to help researchers target components of the underlying crop genetics for engineering, to ultimately enhance crop yield and nutritional quality.
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