Below is a list of some of the previous research projects from the EnBiSys Lab. This list is not comprehensive, but does highlight some of the more recently completed EnBiSys projects.

Convergence Informatics and Data Access for the Science and Technologies for Phosphorus Sustainability (STEPS) Center

The STEPS Center is an NSF Science and Technology Center focused on phosphorus sustainability through the integration of research across disciplines. As one of STEPS research initiatives, convergence informatics seeks to bridge the gaps between their research projects using integrative data management coupled with data-driven models and ML-based approaches. The EnBiSys Lab contributed to STEPS convergence informatics by developing knowledge graphs as a visual representation of STEPS data, where these knowledge graphs capture how data is connected across multiple scales and projects. The goal was to create a dynamic, user-friendly website for STEPs researchers to search, compare and visualize the data collected across STEPS projects and disciplines.

Application of Deep Learning Algorithms for Multiple Environment Soybean Performance Predictions

In collaboration with BASF, this project aimed to predict soybean yield production from genotype, phenotype, and environmental data. The EnBiSys lab used state-of-the-art deep learning algorithms to provide efficient and explainable prediction results for optimal planting solutions for BASF.

Exploring Translation Mechanisms Through Upstream Open Reading Frames in Plants

The goal of this project was to analyze open reading frames (uORFs) within the 5’ leader sequences and explore their impact on translation efficiency. It aimed to enhance our comprehension of the mechanisms influencing uORFs activity. The outcomes of this research guided decisions in genomic breeding. The EnBiSys lab applied machine learning methods to characterize the properties associated with uORFs.

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

The Dorsal protein plays a vital role in the differentiation (patterning) of cells in Drosophila. The accurate measurement of the dorsal gradient is important to gain a better system level understanding of the developmental biology, signaling dynamics and gene regulation. The overall objective of this project was 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 tested the central hypothesis of whether these models necessarily contain “sloppy parameters,” (wide ranges of estimated parameters that fit the experimental data) and led to discovering additional aspects that will improve the model. Our research also included our general knowledge of the NF-κB signaling module that can be found in animals from Cnidarians to humans. Our lab implemented computer vision and image processing techniques to quantify the spatiotemporal behavior of dorsal gradient and build predictive models using mathematical modeling of the parameters involved in this process. This research was performed in collaboration with the Reeves Lab at Texas A&M Univeristy.

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

The goal of this project was 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 is being used to identify and characterize the signal integration hubs and CREs that differentiate transcriptional and translational control. The EnBiSys lab helped to develop molecular network inference approaches for this project, as well as exploring feature spaces and explanatory predictive models to identify elements when only a single dataset is available or appropriate to use.

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

In collaboration with the Reeves Lab at Texas A&M Univeristy, this project aimed 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. The EnBiSys lab assisted in the characterization of the dynamics of the GAP gene transcriptional network responsible for AP patterning by developing computer vision approaches for feature extraction and non-linear systems modeling and analysis.

Collaborative Research: Modeling the regulatory network of InsP6 signaling in plants

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.

Implementation and Analysis of Novel Real-Time QRS Detection Algorithms

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.

INSPIRE: Dynamic Regulatory Modeling of the Iron Deficiency Response in Arabidopsis thaliana

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 modeling the responsible network by developing the optimal inference algorithm.

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

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.

Regulation and Modeling of Lignin Biosynthesis

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.