Department of Electrical and Computer Engineering
Data-Driven Plant Science Platform Director, N.C. PSI
College of Engineering
College of Agriculture and Life Sciences
Department of Plant and Microbial Biology
3320 Plant Sciences Building
Research Interests: I am currently the director of the EnBiSys Research Laboratory. The EnBiSys Lab is a highly collaborative, multidisciplinary research laboratory, focused on the development of targeted computational and analytical solutions for modeling and controlling biological systems. The solutions we develop are used to build and strengthen the transition from large-scale high-throughput –omics data to highly connected kinetic models in the post-genomic era; models that can be used to attain the depth, understanding, and comprehension needed to manipulate and control biological systems for a defined purpose.
Specific interests in this field include:
– Nonlinear Systems Analysis
– System Identification
– Uncertainty Analysis
– Optimal Experimental Design
– Biological Signal and Data Processing
Patents: S. Chen, L. Ray, N. Cahill, M. Goodgame, and C. Williams, “Method of Image Registration using Mutual Information,” U.S. Patent 7,263,243, Aug. 28, 2007.
Ph.D. Electrical Engineering North Carolina State University 2008
M.S. Electrical Engineering North Carolina State University 2002
B.S. Electrical Engineering NC A&T State University, Greensboro 2001
Area(s) of Expertise
Computational Intelligence, Machine Learning, Dynamic Systems Modeling, Multi-scale Modeling, Data Mining, Gene Regulatory Networks, Metabolic Pathway Modeling
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies , CURRENT OPINION IN PLANT BIOLOGY (2023)
- FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus , Plant Physiology (2023)
- Dynamics of BMP signaling in the earlyDrosophilaembryo , (2022)
- Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs , VIRUSES-BASEL (2022)
- POPEYE intercellular localization mediates cell-specific iron deficiency responses , PLANT PHYSIOLOGY (2022)
- Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras , OPTICS EXPRESS (2022)
- A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa , COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)
- Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery , Computers and Electronics in Agriculture (2021)
- Enzyme Complexes of Ptr4CL and PtrHCT Modulate Co-enzyme A Ligation of Hydroxycinnamic Acids for Monolignol Biosynthesis in Populus trichocarpa , FRONTIERS IN PLANT SCIENCE (2021)
- High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales , EMERGING TOPICS IN LIFE SCIENCES (2021)
The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.
A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain
One of the grand challenges facing humanity is to secure sufficient and healthy food for the increasing world population. This requires maintaining sustainable cultivation of crop plants under changing climate conditions. Plant roots and soil microbes have been associated since the emergence of plants on land. Nevertheless, the mechanisms that coevolved to control and regulate microbiota associations with healthy plants are largely unexplored. The photosynthetically active green leaf tissues supply assimilated carbon to roots for development and also to feed its associated microbes. To maintain balanced growth, plants have to integrate this underground demand and regulate the rate of photosynthetic CO2 fixation, and sugar allocation needs to be coordinated between root and shoot. Research on plants and their naturally associated microorganisms is therefore in a prime position to provide new perspectives and concepts for understanding plant function, plant performance and plant growth under limited input conditions with a reduced environmental footprint and could also define breeding targets and develop microbial interventions. InRoot aims to: 1. Disentangle the effects of climate and soil type from the impact of root-microbe interactions through transplantation experiments and exploit natural variation to identify the plant genetic components responsible for adaptation to the local microbiota. 2. Identify key bacterial taxa governing the establishment of host-driven microbial networks in the rhizosphere by analysing the microbe-microbe and microbe-host interactions established in tailored synthetic communities (SynComs) with direct consequences on host performance. 3. Define the plant genetic components that control infection of plant roots by ubiquitous and host-specific endophytes using advanced genetic screens and new methods for quantifying root cellular responses to microbes 4. Understand molecular mechanisms integrating root-microbe interactions into whole-plant physiology by investigating systemic physiological responses induced by SynComs using whole plant phenotyping. 5. Predict plant performance as a function of plant and microbiota genotypes by building multiscale models based on genotype, phenotype, and mechanistic data thereby providing knowledge for application. InRoot perspective: Provide knowledge and tools for science-based development of new crop varieties and associated microbial interventions that will improve productivity, reduce the need for fertilizers and pesticides, and alleviate negative environmental impact.
The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North CarolinaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s sweet potato growers.
Title: Transcriptional and translational regulatory networks of hormone signal integration in tomato and Arabidopsis. PI: Jose M. Alonso (Plant Biology, NCSU), Co-PIs:Anna Stepanova (Plant Biology, NCSU), Steffen Heber (Computer Science, NCSU), Cranos Williams (Electric Engineering, NCSU). Overview: Plants, as sessile organisms, need to constantly adjust their intrinsic growth and developmental programs to the environmental conditions. These environmentally triggered ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œadjustmentsÃƒÂ¢Ã¢â€šÂ¬Ã…â€œ often involve changes in the developmentally predefined patterns of one or more hormone activities. In turn, these hormonal changes result in alterations at the gene expression level and the concurrent alterations of the cellular activities. In general, these hormone-mediated regulatory functions are achieved, at least in part, by modulating the transcriptional activity of hundreds of genes. The study of these transcriptional regulatory networks not only provides a conceptual framework to understand the fundamental biology behind these hormone-mediated processes, but also the molecular tools needed to accelerate the progress of modern agriculture. Although often overlooked, understanding of the translational regulatory networks behind complex biological processes has the potential to empower similar advances in both basic and applied plant biology arenas. By taking advantage of the recently developed ribosome footprinting technology, genome-wide changes in translation activity in response to ethylene were quantified at codon resolution, and new translational regulatory elements have been identified in Arabidopsis. Importantly, the detailed characterization of one of the regulatory elements identified indicates that this regulation is NOT miRNA dependent, and that the identified regulatory element is also responsive to the plant hormone auxin, suggesting a role in the interaction between these two plant hormones. These findings not only confirm the basic biological importance of translational regulation and its potential as a signal integration mechanism, but also open new avenues to identifying, characterizing and utilizing additional regulatory modules in plants species of economic importance. Towards that general goal, a plant-optimized ribosome footprinting methodology will be deployed to examine the translation landscape of two plant species, tomato and Arabidopsis, in response to two plant hormones, ethylene and auxin. A time-course experiment will be performed to maximize the detection sensitivity (strong vs. weak) and diversity (early vs. late activation) of additional translational regulatory elements. The large amount and dynamic nature of the generated data will be also utilized to generate hierarchical transcriptional and translational interaction networks between these two hormones and to explore the possible use of these types of diverse information to identify key regulatory nodes. Finally, the comparison between two plant species will provide critical information on the conservation of the regulatory elements identified and, thus, inform research on future practical applications. Intellectual merit: The identification and characterization of signal integration hubs and cis-regulatory elements of translation will allow not only to better understand how information from different origins (environment and developmental programs) are integrated, but also to devise new strategies to control this flow for the advance of agriculture. Broader Impacts: A new outreach program to promote interest among middle and high school kids in combining biology, computers, and engineering. We will use our current NSF-supported Plants4kids platform (ref) with a web-based bilingual divulgation tools, monthly demos at the science museum and local schools to implement this new outreach program. Examples of demonstration modules will include comparison between simple electronic and genetic circuits.