Dr. Cranos Williams
Goodnight Distinguished Professor of Agricultural Analytics
Platform Director, Data-Driven Plant Sciences Platform, NC Plant Sciences Initiative
NC State University
Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University with primary and secondary appointments in the electrical and computer engineering and plant and microbial biology departments, respectively. Dr. Williams also serves as the Platform Director of the Data-Driven Plant Sciences research platform of the North Carolina Plant Sciences Initiative and is the head of the EnBiSys Research Laboratory. He received his B.S. in electrical engineering from North Carolina A&T State University in 2001, and his M.S. and Ph.D. in electrical engineering from North Carolina State University in 2002 and 2008, respectively.
Dr. Williams has developed a highly collaborative, multidisciplinary research program that focuses on the development of computational and analytical solutions for modeling and understanding the combinatorial interactions of biomolecular, physiological, and structural processes that impact plant growth, development, and adaptation. Specific research contributions include: development of heterogeneous data management and analytics pipelines for decreasing waste and maximizing value across the sweetpotato value chain, multi-scale modeling and machine learning approaches to elucidate plant/microbiome relationships that increase plant resiliency, computer vision tracking of gene expression data over space and time to extract spatial and temporal metrics that assess response of plants to combinatorial stresses, dynamic modelling of gene regulatory networks in plants in response to iron deficiency stress, multiscale modelling of the plant secondary cell wall, system identification and experimental design for biological processes in the presence of bounded uncertainty, data-driven modularization of biological systems to identify individual components and modules in biochemical pathways that influence plant growth, development, and adaptation. The findings from these projects have provided better insight into the molecular mechanisms associated with stress response in plants, yielded models that predict wood formation in trees, used early stakeholder engagement to develop translatable IoT and big-data analytics frameworks that provide solutions to ag- stakeholders, and provided computational algorithms for spatial and temporal tracking of gene expression, molecular experimental design, and multi-scale modeling of biological processes. These findings will ultimately translate to strategies for improving tolerance of crop plants to pathogens and abiotic stresses, increasing the efficiency of biofuel production from plant biomass, and strategies for addressing food security challenges.
Harnessing the Ag data revolution for modeling plant and agronomic systems across scale
The next revolution in precision agriculture solutions will require an improved understanding of the complex regulatory mechanisms that are instrumental in plant growth, development, and adaptation. Key in these efforts is the ability to acquire and analyze data across biological scales (from molecular to phenotypic scales). High-throughput data that have been collected across biological scales include molecular data such as gene expression profiles and confocal imaging to data capturing plant physiology such as hyperspectral imaging and remote sensing. The diversity of these datasets (in combination with the complexity of plant systems) has created opportunities to develop novel computational intelligence and machine learning approaches that are capable of modeling plant systems within and across biological scales. In this presentation, we provide a brief overview of approaches for analyzing various types of high-throughput biological data. These approaches address the many challenges associated with analyzing biological data, including the need to mitigate high variation and/or uncertainty in data, the need for novel segmentation and feature extraction, and the integration of disparate datasets for making causal inferences across scale. The application of these approaches has led to scientific contributions such as the modeling of key gene regulatory mechanisms involved in plant stress response, the identification of emergent properties that link molecular activity to phenotypic outcomes, and the development of automated high-throughput phenotyping approaches for early detection of plant diseases. The continued acquisition of high-throughput data across scale and the continued development of novel machine learning and modeling tools will provide opportunities to further push the boundaries of our understanding of plant systems and will be key to a better understanding of how plants respond to complex environments.
S. Haque, N. Nelson, E. Lobaton, G. C. Yencho, K. Pecota, R. Mierop, M. Kudenov, M. Boyette, and C. Williams, "Computer vision approach to characterize size and shape phenotypes of horticultural crops using high- throughput imagery,", Computers and Electronics in Agriculture, vol. 182, 2021, https://doi.org/10.1016/j.compag.2021.106011.
M. Matthews, J. Wang, R. Sederoff, V. L. Chiang, and C. Williams, “A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa,” Computational and Structural Biotechnology Journal, vol. 19, pp. 168-182, 2020.
A. Koryachko, A. Matthiadis, S. Haque, D. Muhammad, J. Ducoste, J. Tuck, T. Long, C. Williams, “Dynamic modeling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots,” in silico Plants, Volume 1, Issue 1, 2019, diz005, https://doi.org/10.1093/insilicoplants/diz005. (CW and TL co-corresponding authors)
E. Buckner, I. Madison, H. Chou, C.E. Melvin, R. Sozzani, C. Williams, T.A. Long,“ High Resolution Spatiotemporal Imaging and Analysis of Dynamic Cell Cycle Progression Patterns Under Iron Deficiency and Heat Stress Conditions,” Frontiers in Plant Science, vol. 10, pp. 1487, 2019. (CW and TL co-corresponding authors)
J. Wang, P. Naik, H. Chen, R. Shi, C. Lin, J. Jiu, C. Shuford, J. Ducoste, Q. Li, C. Williams, D. Muddiman, R. Sederoff, and V. Chiang, “Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa, ” The Plant Cell Online, vol. 26, no. 3, pp. 894-914, 2014.
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