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.
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.