ABSTRACT
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 existing germplasm in response to global change, and also to 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 Crops in silico (Cis) computational framework. We used the Cis multi-scale modeling platform to: i) integrate models of gene expression, photosynthetic metabolism, and leaf physiology to evaluate the effect of photosynthesis and transpiration under various environmental conditions; and ii) combine modeling and advanced visualization approaches to make direct observations about changes in plant structure, light capture, biomass, and yield in response to environmental perturbations. Outcomes of these efforts include i) accurate prediction of soybean photosynthesis under high atmospheric [CO2]; ii) identification of transcription factors that potentially regulate photosynthesis; and iii) simulated light capture in a 3D soybean canopy. The improved accuracy of model predictions and the realistic rendering of model simulated plants is a step toward the in silico “testing” of ideotype designs under different environmental conditions. This enables researchers to perform dozens of in silico perturbations to evaluate ideotype performance under varying scenarios. 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.
BIOGRAPHY
The focus of Dr. Marshall-Colon's research is to explore the regulatory mechanisms controlling nitrogen uptake and assimilation in plants using a systems biology approach. The overarching goal of my research program 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.
Website: https://publish.illinois.edu/marshall-colon/
