Biosensor Arrays for Molecular Source Detection in Mass-Transport Systems
Aug 12, 2014
from 12:30 PM to 02:00 PM
|Where||Engr. IV Bldg., Maxwell Room 57-124|
|Contact Name||Prof. Aydogan Ozcan|
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University of British Columbia
Biosensors have a wide range of applications in medicine, clinical diagnosis, and environmental monitoring for detection of target molecules or hazardous materials. In this talk, I will present my PhD work which was in collaboration with Surgical Diagnostics Ltd. in Australian to develop highly sensitive ion channel switch biosensor arrays for detection of bio molecular species. Dynamical semi-analytical models were developed which can be used for optimal design and performance analysis of biosensor arrays. The derived models were used to design an algorithm for estimation of molecular concentration. The achievable estimation improvement based on multiple sensors was quantified and the results were illustrated for ion channel switch biosensors. Furthermore, the low sensitivity of biosensor to minute concentrations (nano to pico molar) was addressed. It was shown that substantial improvement can be made by distributing the sensing surface area to form an array of spaced smaller sensors while the total sensing area remains fixed. By deriving formulas, the improvement in the capture rate of bio molecules was quantified and the size of biosensors was optimized. The developed models and results in this work are not specific to ion channel switch biosensors and can be used in general to any type of reactive surface-based sensor. Some of the results were illustrated for surface Plasmon resonance biosensor.
Maryam Abolfath-Beygi received her Bachelors and Master of Science in Electrical engineering at the University of Tehran in Iran. She finished her PhD in Electrical Engineering at the University of British Columbia in Vancouver, Canada in December 2013. In her PhD, she worked on modeling biosensors and applying signal processing algorithms on biosensor measurements. Her research interest spans from statistical signal processing and statistical modeling to dynamical modeling using differential equations.