Projects

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 Plants in silico (Psi) 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 in collaboration with:

Steve Long: http://www.life.illinois.edu/long/

James O’Dwyer: https://publish.illinois.edu/odwyerlab/

Diwakar Shukla: http://shuklagroup.org/

National Center for Supercomputing Applications (NCSA): http://www.ncsa.illinois.edu/

National Data Service (NDS): http://www.nationaldataservice.org/

 

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.

This project is in collaboration with:

Gloria Coruzzi: http://coruzzilab.bio.nyu.edu/

 

A simplified systems genetics pipeline to predict genome-phenome relationships across data-rich and data-poor crop species

Characterization of genomic, transcriptomic, proteomic, and metabolomic responses to environmental stresses could provide novel opportunities to identify important mechanisms underlying stress response in crops. In this project we aim to establish evidence-based statistical network models that can be applied to quantify the collective role that each “-omic” level plays in plant responses to abiotic stress. The main objective is to establish effective strategies that can be used to accurately predict agronomically-important crop traits that are particularly responsive to environmental stresses in both data rich and data poor agronomic species (soybean and canola). We are currently developing an analytical pipeline to identify genomic loci associated with transcriptomic, proteomic, and metabolomic variation under different environmental conditions, then use model simplification to determine the minimal amount of data needed to make accurate predictions.

This project is in collaboration with:

Alex Lipka: http://cropsci.illinois.edu/directory/alipka

 

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 in collaboration with:

Katy Heath: http://www.life.illinois.edu/heath/Heath_Lab/HOME.htm