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 efficientbiological 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

Step1: Engineering E.coli to see light

Levskaya et al., Nature 2005

Bla

ck P

igm

en

t

Patterning bacterialgene expression with light

Levy, Tabor, Wong. IEEE SPM 2006

Step 2: Bacterial photography

ImageMask

BacterialLawn

‘Blind’E.coli

Levskaya et al., Nature 2005

Bacterial portraiture

Escherichia EllingtonE.coli self-portraitPhoto: Marsha Miller

Levskaya et al., Nature 2005

Bacterial films show continuous input-output response

Light Intensity

Outp

ut

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 isincomplete

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

• Zack Simpson (UT-Austin)• Aaron Chevalier (UT-Austin)• Edward Marcotte (UT-

Austin)• Andy Ellington (UT-Austin)• Anselm Levskaya• Chris Voigt