Research
Climate change is rapidly changing the habitability of many species on Earth. Plants have a big role in slowing down climate change as major carbon sequesters, solar energy harvesters, and food providers for many species including humans. Plant metabolism underpins many traits that improve plant productivity but there are many holes in our understanding of how it works as a system. To quantify, model, predict, and engineer desirable metabolic traits such as to maximize biomass production under suboptimal conditions or reallocation of biomass from carbohydrates to lipids, we must decode the complex metabolic networks. Subcellular compartmentation of metabolic reactions through the locations of enzymes is critical to understanding, modeling, and engineering plant metabolism. Yet, the localization of the majority of the predicted enzymes are not yet known. The paucity of experimentally validated information in most plants severely limits the ability of scientists and engineers to assess the performance and translatability of computational tools and resources.
Our research team will develop an integrated pipeline that combines computational prediction, metabolic network modeling, and high-throughput experimental testing using state-of-the-art technologies in live confocal imaging, nanomaterial-mediated plant transformation, and metabolic network modeling.
Using the pipeline, we will create a high-quality subcellular map of small molecule metabolism in Sorghum and Brachypodium. The team will then create accurately compartmentalized metabolic network models and experimentally validate them by measuring a series of outputs in response to environmental challenges and by knocking out gene expression in somatic tissue using carbon nanotube-mediated CRISPR technology. This will be the first time such a large-scale, high-resolution imaging-based localization information of metabolic enzymes is generated for any plant. This dataset will have long-lasting value for benchmarking machine learning algorithms for localization prediction and image annotation. Moreover, the cellular view of metabolism at this scale has not been approached before. |
This project has several novel components that could have a wide impact on a range of fields such as Artificial Intelligence, Metabolic Engineering, Cell Biology, Computational Biology, Plant Science, and Biochemistry. It will generate ground-truth data for subcellular locations of metabolic enzymes, develop a novel integrative pipeline for accelerating discovery, further develop a novel plant transformation technology with potentially broad application, and yield important insights into the structure and function of metabolic networks in both a biofuel model plant and a biofuel crop. Finally, the novel approach of using carbon nanotubes to transform somatic cells in the leaf meristem to generate clonal sectors and perform metabolic network modeling and validation can rapidly test the validity of the models and enable rapid cycles of metabolic engineering for important traits.