Danielle Okerblom

Institution: 
Cuesta College
Year: 
2009

Characterization of Bacteria Biosensor

The common methods for identifying bacteria are slow and outdated. The detection time takes a minimum of 24 hours. In the common method the infected sample is inoculated into a nutrient media where it grows into a colony. This larger population can be used to identify the pathogen using a variety of methods. A faster method of detecting common bacteria found in water samples are available but, these are species specific and do not yield positive results in the presents of other bacteria. Another method is to extract DNA from the bacteria and process it with electrophoresis. This process can be done same day but is not widely implemented because it requires complicated processing of the bacteria to isolate the DNA. There is a need for a new method that can detect bacteria through a range of samples and can yield same day results. Our goal is to design a molecular biosensor which can detect the pathogen and is sensitive to smaller colonies of bacteria; much less than a culture. Our method hopes to eliminate the need to incubate bacteria for detection and identification. The biosensor will use spectral patterns of the electrostatic interactions to identify the bacteria. The interaction pattern is measured with FRET (Forester Resonance Energy Transfer.) Currently we are working to optimize the biosensor. The limitations are that the predictability of biosensor signal has not been established. We are experimenting with a variety of background solvents to optimize the fluorescence signal. Optimal background solvents will encourage the formation of molecular aggregations and facilitate energy transfer. The biosensor has two parts 1. the oligomer with an aromatic backbone and 2. a peptide with a fluorescent tag. The peptide to oligomer concentrations are determined by charge ratio. The most effective oligomer to peptide ratio is 1.75(+):1(-) charges. These ratios effectively form aggregates based on charge for the biosensor. Once the biosensor provides a stable signal then we can establish patterns for different species of bacteria.

UC Santa Barbara Center for Science and Engineering Partnerships UCSB California NanoSystems Institute