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: – 2 24(hr)/20(min) =2 72 =4x10 21 –~10 11 transistors in a PC –~10 10 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 Black Pigment

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 Escherichia Ellington E.coli self-portrait Photo: Marsha Miller Levskaya et al., Nature 2005

Bacterial films show continuous input-output response Light Intensity Output 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 Zack Simpson (UT-Austin) Aaron Chevalier (UT-Austin) Edward Marcotte (UT-Austin) Andy Ellington (UT-Austin) Anselm Levskaya Chris Voigt