Programming Bacterial
Communities to Function as
Massively Parallel Computers
Jeff Tabor
Voigt Lab
University of California, San Francisco
Cells can perform
logical computations
Biological computers
are slow and noisy
To engineer an efficient
biological computer…
• Choose a problem which is
– Computationally simple
– Scales well with many parallel processors
• Number of bacterial computers that
can be grown inexpensively in one day:
– 224(hr)/20(min)=272=4x1021
– ~1011 transistors in a PC
– ~1010 PCs worth of computational power
• Image Processing
– Amenable to parallel efforts (many independent variables)
c/o Zack B. Simpson
Bacterial edge detector
Projector
Petri dish
Steps to engineering a
bacterial edge detector
1.
Make blind E.coli ‘see’
2.
Engineer a bacterial ‘film’
3.
Program film to compute
light/dark boundaries
Black Pigment
Step1: Engineering E.coli to see light
Levskaya et al., Nature 2005
Patterning bacterial
gene expression with light
Levy, Tabor, Wong. IEEE SPM 2006
Step 2: Bacterial photography
Image
Mask
Bacterial
Lawn
‘Blind’
E.coli
Levskaya et al., Nature 2005
Bacterial portraiture
E.coli self-portrait
Photo: Marsha Miller
Escherichia Ellington
Levskaya et al., Nature 2005
Output
Bacterial films show continuous
input-output response
Light Intensity
Levskaya et al., Nature 2005
Continuous response allows
grayscale fidelity
Conclusions – Bacterial Photography
• Theoretical resolution of 100 Megapixels per square
inch
– 10x higher than modern high-resolution printers
• Direct printing of biological materials
– Spider silks
– Metal precipitates
• Light offers exquisite spatiotemporal control
– Spatial: Chemical inducers diffuse
– Temporal: Chemical inducers must decay
Genetic circuit for
edge detection
Only occurs at
light/dark boundary
LOW output from gate 1 interpreted
as HIGH input at gate 2
Light
inhibition is
incomplete
Matching gates through RBS redesign
Step 3: Bacterial Edge Detection
Bacterial Edge Detection
Conclusions – Edge Detector
• Scale-free (size-independent) computation time
– Quadratic scaling in serial computers
• Largest de novo synthetic genetic system to date
– 17.7kb
• Communication facilitates transition from simple single
cell logic to emergent community-level behaviors
Acknowledgements
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Zack Simpson (UT-Austin)
Aaron Chevalier (UT-Austin)
Edward Marcotte (UT-Austin)
Andy Ellington (UT-Austin)
Anselm Levskaya
Chris Voigt