Crops in Silico
* Free, public seminar — all welcome *
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 various crops in response to climate and resource availability, and also aid in the design of crop ideotypes.
We 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.
The Cis multi-scale modeling platform has been used to:
- Integrate models of gene expression, photosynthetic metabolism, and leaf physiology to evaluate the effect of photosynthesis and transpiration under various environmental conditions.
- Link a functional-structural root model to a process-based canopy model of maize to explore crop response to environmental inputs.
- Combine modeling and advanced visualisation 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
- enhanced estimates of sink-source dynamics in maize under nutrient limited conditions, and
- refined canopy-level photosynthesis rate predictions due to a more accurate simulation of leaf area and leaf angle using 3D visualisation tools.
Additionally, the technical developments in support of these goals have included new mechanisms for intra- and inter-disciplinary model communication, visualisation, 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.
Speaker bio's
Amy Marshall-Colon is an assistant 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.
Matthew Turk is an assistant professor in the School of Information Sciences at the University of Illinois, with an appointment in the Astronomy department. He received his PhD from Stanford University in Physics, and is involved in a number of open source and open science initiatives, including yt (yt-project.org), whole tale (wholetale.org) and crops in silico.