Electronic Interfaces for Bacteria-Based Biosensing

Tom Zajdel

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2018-129
August 28, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-129.pdf

Bacterial sensing systems have evolved to detect complex biomolecules, operating near fundamental physical limits for biosensing. No modern engineered biosensor has managed to match the efficiency of bacterial systems, which optimize for each sensing application under constraints on response time and sensitivity. An emerging approach to address this shortfall is to build biosensors that electronically couple microbes and devices to combine the sensing capabilities of bacteria with the communication and data processing capabilities of electronics. This dissertation presents three techniques that advance engineering at the interface between bacteria and electronics, all working towards the integration of living material into hybrid biosensing platforms. In the first technique, we embed current-producing Shewanella oneidensis inside a conductive PEDOT:PSS matrix to electronically interface and structure the bacteria into 3D conductive biocomposite films to our specifications. In the second technique, we observe large numbers of chemotactic bacterial flagellar motor (BFM) behavior to infer environmental conditions, using machine learning to co-opt Escherichia coli's motor response for the front end of a biosensor. In the final technique, we demonstrate progress towards a method to electronically monitor BFM rotation over time for electrochemical biosensing. Together, this body of work contributes to more functional interfaces between silicon- and carbon-based materials for advanced biosensing applications including persistent in situ environmental sensing and microbiorobotics.

Advisor: Michel Maharbiz


BibTeX citation:

@phdthesis{Zajdel:EECS-2018-129,
    Author = {Zajdel, Tom},
    Title = {Electronic Interfaces for Bacteria-Based Biosensing},
    School = {EECS Department, University of California, Berkeley},
    Year = {2018},
    Month = {Aug},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-129.html},
    Number = {UCB/EECS-2018-129},
    Abstract = {Bacterial sensing systems have evolved to detect complex biomolecules, operating near fundamental physical limits for biosensing. No modern engineered biosensor has managed to match the efficiency of bacterial systems, which optimize for each sensing application under constraints on response time and sensitivity. An emerging approach to address this shortfall is to build biosensors that electronically couple microbes and devices to combine the sensing capabilities of bacteria with the communication and data processing capabilities of electronics. This dissertation presents three techniques that advance engineering at the interface between bacteria and electronics, all working towards the integration of living material into hybrid biosensing platforms. In the first technique, we embed current-producing Shewanella oneidensis inside a conductive PEDOT:PSS matrix to electronically interface and structure the bacteria into 3D conductive biocomposite films to our specifications. In the second technique, we observe large numbers of chemotactic bacterial flagellar motor (BFM) behavior to infer environmental conditions, using machine learning to co-opt Escherichia coli's motor response for the front end of a biosensor. In the final technique, we demonstrate progress towards a method to electronically monitor BFM rotation over time for electrochemical biosensing. Together, this body of work contributes to more functional interfaces between silicon- and carbon-based materials for advanced biosensing applications including persistent in situ environmental sensing and microbiorobotics.}
}

EndNote citation:

%0 Thesis
%A Zajdel, Tom
%T Electronic Interfaces for Bacteria-Based Biosensing
%I EECS Department, University of California, Berkeley
%D 2018
%8 August 28
%@ UCB/EECS-2018-129
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-129.html
%F Zajdel:EECS-2018-129