Biologically Inspired Wireless Sensor Networking
Department
Electrical and Computer Engineering
Major
Computer Engineering
Research Advisor
Das, Sajal K.
Advisor's Department
Computer Science
Funding Source
National Science Foundation
Abstract
Gene regulatory networks (GRNs) involve the interactions of proteins in collections of DNA segments. Understanding GRNs has many applications in both biological and computational sciences, ranging from a potential cure for cancer to solving problems incurred by maintaining large scale sensor networks. The study of the GRNs has boomed over the past several years, as DNA sequencing technology has progressed, unlocking the potential of understanding the GRN by analyzing the regulatory process more in-depth. The objective of this research is to analyze the graph representations of E. coli and yeast GRNs for the purpose of creating similar scale-free networks able to be used for robust, efficient wireless sensor networking.
Biography
Alec Bayliff is a senior in computer engineering at the Missouri University of Science and Technology. He is the robotic arm team lead for the Telemetry and Controls division of the Missouri S&T Mars Rover Design Team. Alec also participates as an officer of the Institute of Electrical and Electronics Engineers as well as Miners in Space. Alec recently completed an REU with CReWMaN labs in the computer science department at Missouri S&T.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hall
Presentation Date
15 Apr 2015, 9:00 am - 11:45 am
Biologically Inspired Wireless Sensor Networking
Upper Atrium/Hall
Gene regulatory networks (GRNs) involve the interactions of proteins in collections of DNA segments. Understanding GRNs has many applications in both biological and computational sciences, ranging from a potential cure for cancer to solving problems incurred by maintaining large scale sensor networks. The study of the GRNs has boomed over the past several years, as DNA sequencing technology has progressed, unlocking the potential of understanding the GRN by analyzing the regulatory process more in-depth. The objective of this research is to analyze the graph representations of E. coli and yeast GRNs for the purpose of creating similar scale-free networks able to be used for robust, efficient wireless sensor networking.
