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Tom Zajdel, Ph.D. 2017

Electrical Engineering
Advisor: Prof. Maharbiz

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Research Interests: Systems biology, probabilistic systems modeling, microbial electrophysiology, and microbiorobotics.
Job Interests: Academic research and teaching positions in the areas of biological system theory and simulation.

I am a PhD student advised by Michel Maharbiz in the department of Electrical Engineering and Computer Science at UC Berkeley. I work on developing methods to engineer and control microbial ensembles, which touches on system theory, microbial electrophysiology, and synthetic biology.
I received my B.S. in Electrical and Computer Engineering at the Ohio State University in 2008, with emphasis on signal processing. My inquiries have led me to a diverse set of experiences including work with diaper production lines, anti-mortar radar, electromagnetic wave scattering, and cochlear implant speech processing before my present focus on biological work. In addition, I teach as I find it a critical and rewarding part of a knowledge-based career.

Direct Electron-Mediated Control of Hybrid Multi-Cellular Robots [BPN718]
We propose to design, fabricate and test a millimeter-scale, programmable cellular- synthetic hybrid robot capable of autonomous motility, sensing and response in aqueous environments. Three integrated technologies will make this possible: 1) two-way electron transfer between an electrode and E. coli for rapid communication between abiotic core and cells; 2) a flexible polymer + CMOS sensing and computation abiotic core; 3) synthetic cell adhesion genes which allow for patternable self-assembly of bacterial cells onto the abiotic substrate. If successful, this will be the first demonstration of a millimeter-scale synthetic autonomous multi-cellular hybrid with organic and man-made components. A primary goal of this work will be to enable abiotic/biotic two- way communication via electron transfer channels engineered into cells in contact with microelectrodes. We suggest that such a fusion would enable control techniques that rely on combinations of gene expression, cell-level sensing / actuation and CMOS digital computation.

Current Active Projects:

     Last Updated: Thu 2014-Jul-24 16:43:48

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