Improving Crop Productivity and Value Through Heterogeneous Data Integration, Analytics, and Decision Support Platforms

Project Description: The goal of this project is developing new data analysis tools for streamlining the North Carolina sweetpotato industry. A nexus point of all crop value is located at sorting facilities, where produce is sorted into value-added categories. These physical characteristics will be linked to up-stream provenance data (field location, weather data, management practices) and down-stream value (consumer preferences, storage life, etc.) to develop new management practices that maximize value. Currently, our lab is developing aspects related to the back-end machine learning classification techniques for quantifying sweetpotato phenotypes.

Contributors: Dr. Samiul Haque, Hangjin Liu, Daniel Katowitz, Stephen Chang.

Systems-level measurements of biophysical parameters in the Dorsal/NF-kappaB pathway

Project Description: The overall objective of this proposal is to perform detailed measurements of local biophysical parameters and global morphogen gradient properties to build and constrain a predictive, computational model of the Dorsal/NF-κB gradient in the early Drosophila embryo. The models generated as part of this project will test the central hypothesis of whether these models necessarily contain “sloppy parameters,” (wide ranges of estimated parameters that fit the experimental data) or may lead to discovering additional aspects that can improve the model. The proposed research will also include our general knowledge of the NF-κB signaling module that can be found in animals from Cnidarians to humans. Our lab is implementing image processing approaches for improving extraction of gene expression data and parameter estimation approaches for system identification.

Contributors: Sharva Hiremath.

Molecular Mechanisms and Dynamics of Plant-Microbe Interactions at the Root-Soil Interface: InRoot

Project Description: The project is one of three projects funded by the Novo Nordisk Foundation under the Collaborative Crop Resilience Program. This is an international collaboration between NC State, The University of Copenhagen, Aarhus University and the Technical Univ. of Denmark. The goal of these projects is to take a systems-level view of plants and their interactions with microbes in the soil, roots and foliage to improve plant health and productivity. This is particularly important in the face of climate change and emerging pathogens and pests where leveraging microbes to help plants avoid stresses while acquiring nutrients to reduce fertilizer, pesticide and irrigation become important. Our lab is developing multiscale models across biological scales that can predict the contribution of key plant and microbe interactions, which mediate intra- and inter-organismic resource allocation and promote plant fitness and resilience.

Contributors: Max Gordan. 

Natural variation and systems-level properties of gene regulation in Drosophila

Project Description: The overall objective in this proposal is to use the natural variation that occurs in a panel of wild-caught fly lines to characterize the GRN responsible for precise anterior-posterior (AP) patterning the early Drosophila embryo. This will test the central hypothesis that gene expression patterns in the AP patterning system have undiscovered regulation that can be found by examining the correlation between gene expression and natural variation in the genomes of these flies. Our lab is characterizing the dynamics of the GAP Gene transcriptional network responsible for AP patterning. These efforts include the development of computer vision approaches for feature extraction and non-linear systems modeling and analysis.

Contributors: Dr. Samiul Haque, Sharva Hiremath.

Identification of Translational Hormone-Response Gene Networks and Cis-Regulatory Elements

Project Description: The goal of this project is to formulate a systems approach for characterizing transcriptional and translational networks in Arabidopsis thaliana in response to ethylene and auxin disruption at the whole-genome scale. This work will be used to identify and characterize the signal integration hubs and cis-regulatory elements that differentiate transcriptional and translational control. Our lab is developing molecular network inference approaches for this project.

Contributors: Haonan Tong.

Collaborative Research: Modeling the regulatory network of InsP6 signaling in plants (Past Project)

Project Description: The goal of the project is to utilize kinetic modeling strategies in combination with model analysis approaches to better understand how various isomers of inositol phosphates, key signaling components that play a role in phosphate absorption, convey signaling information leading to changes in phosphate utilization at different developmental stages in plants (seeds versus leaves). Our lab is modeling the inositol phosphate metabolic pathway using non-linear systems of ordinary differential equations.

Contributors: Cody Ellington.

 

Implementation and Analysis of Novel Real-Time QRS Detection Algorithms (Past Project)

Project Description: The goal of this project was to develop an algorithm for real-time detection of the QRS complex in electrocardiogram (ECG) signals. This algorithm was to be used in an implantable Intra-aortic balloon pump, which is used to mechanically assist heart failure patients. This project was funded through NuPulse, Inc., a startup company in the Research Triangle Park. Our lab is developing the optimal detection algorithm to the sponsor.

Contributors: Shamim Samadi.

INSPIRE: Dynamic Regulatory Modeling of the Iron Deficiency Response in Arabidopsis thaliana (Past Project)

Project Description: The goal of this project is to better understand the mechanisms associated with stress response in the Arabidopsis thaliana roots. The project integrates novel modeling techniques with high-speed computing architectures to identify and model previously uncharacterized regulatory components that control iron homeostasis in Arabidopsis across multiple cell types. Our lab is modelling the responsible network by developing the optimal inference algorithm.

Contributors: Dr. Alexandr Koryachko, Dr. Samiul Haque, Dr. Eli Buckner, Selene Schmittling. 

Modeling of Cellulose, Hemicellulose and Lignin-Carbohydrate Complex Formation and Regulation to Understand Plant Cell Wall Structure (Past Project) 

Project Description: The goal of this project is to improve our fundamental understanding of the plant secondary cell wall. The modeling goal is to develop computational learning strategies that integrate genome-based information and mathematical modeling to identify fundamental relationships between cellulose/hemicellulose regulation and bioenergy traits critical for efficient production of biofuel from plant biomass. Our lab is formulating the computational algorithms needed to establish relationships between transcripts and quantified bioenergy traits.

Contributors: Zohaib Qazi.

 

Regulation and Modeling of Lignin Biosynthesis (Past Project)

Project Description: The goal of this project is to integrate data obtained from P. trichocarpa transgenics to build a predictive model of the regulation and biosynthesis of lignin, a phenylpropanoid polymer that serves as a significant barrier to cellulosic sources of ethanol. Our lab is modeling the regulatory mechanisms.

Contributors: Dr. Jina Song, Dr. Megan Leigh Matthews.