Advancing Bioenergy Feedstock Resilience: Integrative AI and Genomic Solutions for Climate-Adaptive Crop Development
Idaho National Laboratory
Description
At ORNL, together with our collaborators, our laboratory is developing next generation approaches to address the complex challenges presented by climate change for bioenergy feedstock agriculture utilizing species such as pennycress, switchgrass, and poplar. With a multidisciplinary team of researchers, we leverage cutting-edge technologies, including artificial intelligence (AI), genomics, and phenomics, to understand and mitigate the impacts of environmental stresses on plant species vital for bioenergy sustainability. Our work, as demonstrated in our recent publications, showcases our lab’s capabilities in utilizing climatic data clustering at a global scale for longitudinal analysis, which aids in better resource allocation and planning to combat the adverse effects of climate change. We are pioneering methods with which to accelerate the development of current and future climate-resilient plant varieties through the integration of next-generation AI with advanced genetic tools. This approach not only fast-tracks the development of crops capable of withstanding abiotic stresses but also contributes to sustainable agricultural practices and bioenergy production. Our research contributes to a deeper understanding of the long-term effects of environmental changes on plant health and disease emergence, highlighting the importance of continuous monitoring and innovation in developing solutions for future energy security. Our lab is committed to advancing the scientific understanding and technological innovations needed to build resilience against the challenges posed by a changing climate.
Capability Bounds
We are advancing climate-resilient bioenergy feedstocks using AI, genomics, and phenomics. Focusing on pennycress, switchgrass, and poplar, our innovative approaches combat climate change impacts, ensuring sustainable bioenergy production and agriculture by developing stress-resistant plant varieties. These approaches can be extended to many different challenges in bioenergy, carbon sequestration, and sustainability.
Unique Aspects
We are pioneering next-generation solutions for bioenergy agriculture, focusing on climate-resilient crops like pennycress, switchgrass, and poplar. Utilizing AI, genomics, and phenomics, the lab analyzes climate data for strategic resource allocation and enhanced planning against climate change impacts. Specializing in speeding up the design of robust plant varieties, our lab’s work supports sustainable agriculture and bioenergy, while their research deepens the understanding of how environmental stress affects plant health and disease, ensuring future energy security through continuous innovation.
Availability
Our lab collaborates globally, sharing our innovative AI, genomics, and phenomics tools and methods with our partners to foster resilient bioenergy crop development. By pooling resources and expertise, we and our partners can jointly tackle climate change impacts on bioenergy agriculture, promoting sustainable practices and knowledge exchange for collective advancement.
Unique Aspects
Our approaches harness AI and genomics to create robust bioenergy crops, offering a quicker response to climate change, enhancing agricultural sustainability, and improving resource allocation. This leads to stronger plant varieties, better energy security, and resilience against environmental stress, contributing to overall ecosystem and energy sustainability.
Capability Expert(s)
Dan Jacobson
References
1. Lagergren, J., Cashman, M., Melesse Vergara, V., Eller, P., Gazolla, J.G.F.M., Chhetri, H., Streich, J., Climer, S., Thornton, P., Joubert, W., & Jacobson, D. (2022). Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics. *Phytobiomes Journal*, (ja).
2. Harfouche, A., Jacobson, D., Kainer, D., Romero, J., Harfouche, A.H., Scarascia Mugnozza, G., Moshelion, M., Tuskan, G., Keurentjes, J., & Altman, A. (2019). Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. *Trends in Biotechnology*.
3. Streich, J., Romero, J., Gazolla, J.G.F.M., Kainer, D., Cliff, A., Prates, E.T., Brown, J.B., Khoury, S., Tuskan, G.A., Garvin, M., & Jacobson, D., Harfouche, A*. (2020). Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals? *Current Opinion in Biotechnology*, 61, 217-225.
4. Cashman, M., Melesse Vergara, V., Lane, M., Lagergren, J., Merlet, J., Atkinson, M., Streich, J., Bradburn, C., Plowright, R., Joubert, W., & Jacobson, D. (2023). Longitudinal effects on plant species involved in agriculture and pandemic emergence undergoing changes in abiotic stress. *Proceedings of the Platform for Advanced Scientific Computing*, Article No.: 3, 1–10. [https://doi.org/10.1145/3592979.3593402](https://doi.org/10.1145/3592979.3593402)
5. Lagergren, J.H., Pavicic, M., Chhetri, H.B., York, L.M., Hyatt, D., Kainer, D., Rutter, E.M., Flores, K., Bailey-Bale, J., Klein, M., Taylor, G., & Jacobson, D. (2023). Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa. *Plant Phenomics*. DOI: [10.34133/plantphenomics.0072](https://doi.org/10.34133/plantphenomics.0072)
6. O’Banion, B.S., Jones, P., Demetros, A.A., Kelley, B.R., Wagner, A.S., Reynolds, T.B., Chen, J.-G., Muchero, W., Jacobson, D., & Lebeis, S.L. (2023). Plant myo-inositol transport influences bacterial colonization phenotypes. *Current Biology*. [https://doi.org/10.1016/j.cub.2023.06.057](https://doi.org/10.1016/j.cub.2023.06.057)
7. Cope, K.R., Prates, E.T., Miller, J.I., Demerdash, O.N., Shah, M., Kainer, D., Cliff, A., Sullivan, K.A., Cashman, M., Lane, M., Matthiadis, A., Labbé, J., Tschaplinski, T.J., Jacobson, D., & Kalluri, U.C. (2023). Exploring the role of plant lysin motif receptor-like kinases in regulating plant-microbe interactions in the bioenergy crop Populus. *Computational and Structural Biotechnology Journal*, 21, 1122-1139.
8. Liu, Y., Yuan, G., Hassan, M.M., Abraham, P.E., Mitchell, J.C., Jacobson, D., Tuskan, G.A., Khakhar, A., Medford, J., Zhao, C., & Liu, C.J. (2022). Biological and Molecular Components for Genetically Engineering Biosensors in Plants. *BioDesign Research*.
9. Harfouche, A.L., Nakhle, F., Harfouche, A.H., Sardella, O.G., Dart, E., & Jacobson, D. (2022). A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. *Trends in Plant Science*.
10. Walker, A.M., Cliff, A., Romero, J., Shah, M., Jones, P., Gazolla, J.G.F.M., Jacobson, D., & Kainer, D. (2022). Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data. *Computational and Structural Biotechnology Journal*, 20, 3372-3386.
11. Shrestha, V., Chhetri, H.B., Kainer, D., Xu, Y., Hamilton, L., Piasecki, C., Wolfe, B., Wang, X., Saha, M., & Jacobson, D., Millwood, R. (Year not provided). The Genetic Architecture of Nitrogen Use Efficiency in Switchgrass (*Panicum virgatum* L.). *Frontiers in Plant Science*, p.1216.
12. Chhetri, H.B., Furches, A., Macaya-Sanz, D., Walker, A.R., Kainer, D., Jones, P., Harman-Ware, A.E., Tschaplinski, T.J., Jacobson, D., Tuskan, G.A., & DiFazio, S.P. (2020). Genome-Wide Association Study of Wood Anatomical and Morphological Traits in Populus trichocarpa. *Frontiers in Plant Science*, 11, 1391.
13. Cliff, A., Romero, J., Kainer, D., Walker, A., Furches, A., & Jacobson, D. (2019). A High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks. *Genes*, 10(12), 996.
14. Furches, A., Kainer, D., Weighill, D., Large, A., Jones, P., Walker, A.M., Romero, J., Gazolla, F.M., Gabriel, J., Joubert, W., Shah, M., Streich, J., Ranjan, P., Schmutz, J., Sreedasyam, A., Macaya-Sanz, D., Zhao, N., Martin, MZ., Rao, X., Dixon, RA., DiFazio, S., Tschaplinski, T.J., Chen, J., Tuskan, G.A., & Jacobson, D. (2019). Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence. *Frontiers in Plant Science*, 10, 1249.
15. Weighill, D., Tschaplinski, T., Tuskan, G., & Jacobson, D. (2019). Data Integration in Poplar: ‘Omics Layers and Integration Strategies. *Frontiers in Genetics*, 10, 874.
16. Jones, P., Garcia, B.J., Furches, A., Tuskan, G.A., & Jacobson, D. (2019). Plant host-associated mechanisms for microbial selection. *Frontiers in Plant Science*, 10.
17. Weighill, D., Jones, P., Bleker, C., Ranjan, P., Shah, M., Zhao, N., Martin, M., DiFazio, S., Macaya-Sanz, D., Schmutz, J., Sreedasyam, A., Tschaplinski, T., Tuskan, G., & Jacobson, D. (2019). Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships. *Frontiers in Genetics*, 10, 417.
18. Weighill, D., Macaya-Sanz, D., DiFazio, S.P., Joubert, W., Shah, M., Schmutz, J., … & Jacobson, D. (2019). Wavelet-based Genomic Signal Processing for Centromere Identification and Hypothesis Generation. *Frontiers in Genetics*, 10, 487.
19. Chhetri, H., Macaya-Sanz, D., Kainer, D., Biswal, A., Chen, J., Collins, C., Evans, L., Hunt, K., Mohanty, S., Rosenstiel, T., Ryno, D., Winkeler, K., Yang, X., Jacobson, D., Mohnen, D., Muchero, W., Strauss, S., Tschaplinski, T., Tuskan, G., & DiFazio, S. (2019). Multi-trait genome-wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits. *New Phytologist*, doi:10.1111/nph.15777.
