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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 • • • • • • Zack Simpson (UT-Austin) Aaron Chevalier (UT-Austin) Edward Marcotte (UT-Austin) Andy Ellington (UT-Austin) Anselm Levskaya Chris Voigt
