Signal processing for biologically-inspired gradient source localization and DNA sequence analysis

• Main Author: Rosen, Gail L.
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2006
• Subjects: DNA analysis; Ficks second law; Hebbian learning; Biased random walk; Sensor cross-correlation; Delay-and-Sum beamforming; Turbulent plumes; Electronic nose; Tandem repeats; Gradient sensing; Bacterial chemotaxis navigation; Chemotaxis; Biologically-inspired computing; Signal processing; Sensor networks; Nucleotide sequence; Nervous system Degeneration
• Online Access: http://hdl.handle.net/1853/11628

Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified as critical to understanding biology: 1) understanding DNA, 2) examining the overall biological function and 3) evaluating these systems in environmental (ie: turbulent) conditions. DNA is investigated for coding structure and redundancy, and a new tandem repeat region, an indicator of a neurodegenerative disease, is discovered. The linear algebraic framework can be used for further analysis and techniques. The work illustrates how signal processing is a tool to reverse engineer biological systems, and how our better understanding of biology can improve engineering designs. Then, the way a single-cell mobilizes in response to a chemical gradient, known as chemotaxis, is examined. Inspiration from receptor clustering in chemotaxis combined with a Hebbian learning method is shown to improve a gradient-source (chemical/thermal) localization algorithm. The algorithm is implemented, and its performance is evaluated in diffusive and turbulent environments. We then show that sensor cross-correlation can be used in solving chemical localization in difficult turbulent scenarios. This leads into future techniques which can be designed for gradient source tracking. These techniques pave the way for use of biologically-inspired sensor networks in chemical localization.