1. Programming Bacterial Communities to Function as Massively Parallel Computers Jeff Tabor Voigt Lab University of California, San Francisco

2. Cells can perform logical computations

3. Biological computers are slow and noisy

4. 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

5. Bacterial edge detector Projector Petri dish

6. 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

7. Step1: Engineering E.coli to see light Levskaya et al., Nature 2005 Black Pigment

8. Patterning bacterial gene expression with light Levy, Tabor, Wong. IEEE SPM 2006

9. Step 2: Bacterial photography Image Mask Bacterial Lawn ‘Blind’ E.coli Levskaya et al., Nature 2005

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

11. Bacterial films show continuous input-output response Light Intensity Output Levskaya et al., Nature 2005

12. Continuous response allows grayscale fidelity

13. 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

14. Genetic circuit for edge detection Only occurs at light/dark boundary

15. LOW output from gate 1 interpreted as HIGH input at gate 2 Light inhibition is incomplete

16. Matching gates through RBS redesign

17. Step 3: Bacterial Edge Detection

18. Bacterial Edge Detection

19. 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

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