Crops in silico: a multi-scale modeling platform
Tools capable of predicting emergent crop phenotypes in response to climate change are needed to evaluate risk to food security and direct research efforts toward making educated decisions about which metabolic pathways to modify. Individual models are limited in their ability to provide a holistic view of plant or ecosystem response to global change. To address this limitation, we are generating a multi-scale modeling platform called yggdrasil to provide a quantitative knowledge framework where the implications of a discovery at one level can be examined at the whole plant or even crop or natural ecosystem levels. This community effort will generate new insights into plant biology as well as new modalities for collaboration and integration among plant biologists, engineers, and computer scientists.
This project is funded by the Foundation for Food and Agriculture Research (FFAR): Award number 602757
Nitrogen Network Dynamics: Integration of metabolome and transcriptome response to nitrogen signal
For this project, we utilize a systems biology approach to collectively analyze and integrate time-dependent data from the various components of the N-regulatory network controlling N-assimilation in Arabidopsis. This approach allows us to dynamically model the flow of N-signal propagation through the N-regulatory network on a systems-wide level and identify the transcriptional cascade involved in this regulation. To achieve this goal, high-resolution kinetic transcriptome data, metabolite and metabolic flux data generated using 15N, are used to track the flow of N-assimilation products in concert with changes in the N-regulatory network in response to N-treatments and genetic perturbations. By integrating metabolites into the regulatory networks, they can be modeled as both “input signals” regulating gene expression and as “outputs” of the metabolic pathway.
Relevant Publication:
Varala K.*, A. Marshall-Colon*, J. Cirrone*, M. Brooks, A.V. Pasquino, S. Leran, S. Mittal, T. Rock, M.B. Edwards, G.J. Kim, S. Ruffel, W.R. McCombie, D. Shasha, and G.M. Coruzzi. 2018. The temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants. PNAS. https://doi.org/10.1073/pnas.1721487115
Systems genetics of symbiotic quality and environmental response in legume-rhizobium mutualism
Molecular traits are likely targets of natural selection, and are thus integral to a mechanistic understanding of evolutionary change. In this project, we are building network models capable of making gene-to-trait predictions for the ecologically- and evolutionarily- important symbiosis between leguminous plants and nitrogen (N)-fixing rhizobium bacteria. Here, we integrate genome-wide nucleotide variation with phenotypic and molecular estimates of symbiotic partner quality using a range of high- and low-partner quality rhizobia grown in both high-N and low-N soil environments. Our central objective is to use naturally-occurring variation in rhizobia to better resolve the genetic, molecular, and environmental regulation of plant and rhizobium metabolic pathways contributing to mutualism decline in high N environments.
This project is funded by the NSF (Award number 1645875) in collaboration with:
Katy Heath: http://www.life.illinois.edu/heath/Heath_Lab/HOME.htm
Center for Advanced Bioenergy and Bioproduct Innovation (CABBI)
Our work with the DOE funded CABBI BRC is focused on increasing our understanding of grass stem biology. We are using transcriptome and metabolome data to build stem-specific gene networks and identify modes of regulation. By identifying stem-preferred genes and exploring their regulatory promoters, we may be able to synthetically and specifically drive the expression of genes involved in oil biosynthesis and transport in grass stems, while avoiding the negative effects of constitutive promoter expression, such as dwarfing.