1. 1 DNA Computation: The Secret of Life as Non-Living Technology Russell Deaton Professor Comp. Science & Engineering The University of Arkansas Fayetteville, AR 72701 rdeaton@uark.edu

2. 2 We have discovered the secret of life! -Francis Crick, Feb. 28, 1953

3. 3 P HYSICAL S TRUCTURE OF DNA Nitrogenous Base 34 Å Major Groove Minor Groove Central Axis Sugar-Phosphate Backbone 20 Å 5’ C 3’ OH 3’ 0H C 5’ 5’ 3’ 5’

4. 4 DNA in the Cell Stored in Number of Chromosomes (24 in Human Genome) Stored in Number of Chromosomes (24 in Human Genome) Tightly coiled threads of DNA and Associated Proteins Tightly coiled threads of DNA and Associated Proteins 3 billion bp in Human Genome: Total genetic content of cell 3 billion bp in Human Genome: Total genetic content of cell

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6. 6 Genetic Code

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9. 9 Genome Sizes OrganismGenome Size (Bases)Genes Human 3 billion 30,000 Laboratory mouse 2.6 billion 30,000 Mustard weed 100 million 25,000 Roundworm 97 million 19,000 Fruit fly 137 million13,000 Yeast 12.1 million 6,000 Bacterium 4.6 million 3,200 HIV 97009

10. 10 DNA for Non-Biological Purposes Encode Abiotic Information in DNA Sequences Encode Abiotic Information in DNA Sequences Graphs and Structure Graphs and Structure Computational Problems Computational Problems Database and Memory Database and Memory Search and/or Assemble Through DNA-to- DNA Template-matching reactions Search and/or Assemble Through DNA-to- DNA Template-matching reactions Enzymatic Operations for Further Information Processing Enzymatic Operations for Further Information Processing

11. 11 P HYSICAL S TRUCTURE OF DNA Nitrogenous Base 34 Å Major Groove Minor Groove Central Axis Sugar-Phosphate Backbone 20 Å 5’ C 3’ OH 3’ 0H C 5’ 5’ 3’ 5’

12. 12 Template Matching Hybridization Reaction ` A-C-A-A-C-G T-G-T-T-G-C’ ` A-C-A-A-C-G T-G-T-T-G-C’

13. 13 Hybridization Allows: Massively Parallel Search based on Watson-Crick Complements Massively Parallel Search based on Watson-Crick Complements Directed Self-Assembly of Nanostructures Directed Self-Assembly of Nanostructures Search Stored Information for Similar Sequence Content Search Stored Information for Similar Sequence Content

14. 14 Differences to Biology No Proof-reading enzymes No Proof-reading enzymes Cell versus the Test Tube Cell versus the Test Tube Technology Driven by Template-matching reactions between relatively short oligonucleotides Technology Driven by Template-matching reactions between relatively short oligonucleotides Biology: DNA primarily duplex Biology: DNA primarily duplex Abiotic: DNA primarily single-stranded Abiotic: DNA primarily single-stranded

15. 15 How to encode a graph?

16. 16 Algorithm Generate Random Paths through the graph. Generate Random Paths through the graph. Keep only those paths that begin with v in and end with v out. Keep only those paths that begin with v in and end with v out. If graph has n vertices, then keep only those paths that enter exactly n vertices. If graph has n vertices, then keep only those paths that enter exactly n vertices. Keep only those paths that enter all the vertices at least once. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No” In any paths remain, say “Yes”; otherwise, say “No”

17. 17 Representing a Graph with Sequences 0 1 2 ‘GCATGGCC ‘AGCTTAGG ‘ATGGCATG CCGGTCGA’ CCGGTACC’ ‘GCATGGCCAGCTTAGG CCGGTCGA’ ‘GCATGGCCATGGCATG CCGGTACC’ 0021

18. 18 Massively Parallel Search V1V1 E 0->1 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 1->2 E 2->3 E 3->4 E 4->5 E 5->6 V6V6 E 0->6 V0V0 V3V3 E 0->3 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 3->2 E 2->3 E 3->4 E 4->5 E 5->6 V5V5 E 4->5 V4V4 V1V1 V2V2 E 5->1 E 1->2

19. 19 Algorithm Generate Random Paths through the graph. Generate Random Paths through the graph. Keep only those paths that begin with v in and end with v out. Keep only those paths that begin with v in and end with v out. If graph has n vertices, then keep only those paths that enter exactly n vertices. If graph has n vertices, then keep only those paths that enter exactly n vertices. Keep only those paths that enter all the vertices at least once. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No” In any paths remain, say “Yes”; otherwise, say “No”

20. 20 DNA Polymerase

21. 21 POLYMERASE CHAIN REACTION

22. 22 Start = V0, Stop = V6 V1V1 E 0->1 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 1->2 E 2->3 E 3->4 E 4->5 E 5->6 V6V6 E 0->6 V0V0 V3V3 E 0->3 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 3->2 E 2->3 E 3->4 E 4->5 E 5->6 V5V5 E 4->5 V4V4 V1V1 V2V2 E 5->1 E 1->2

23. 23 Algorithm Generate Random Paths through the graph. Generate Random Paths through the graph. Keep only those paths that begin with v in and end with v out. Keep only those paths that begin with v in and end with v out. If graph has n vertices, then keep only those paths that enter exactly n vertices. If graph has n vertices, then keep only those paths that enter exactly n vertices. Keep only those paths that enter all the vertices at least once. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No” In any paths remain, say “Yes”; otherwise, say “No”

24. 24 G EL E LECTROPHORESIS - SIZE SORTING Buffer Gel Electrode Samples Faster Slower

25. 25 Right Length V1V1 E 0->1 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 1->2 E 2->3 E 3->4 E 4->5 E 5->6 V6V6 E 0->6 V0V0 V3V3 E 0->3 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 3->2 E 2->3 E 3->4 E 4->5 E 5->6

26. 26 Algorithm Generate Random Paths through the graph. Generate Random Paths through the graph. Keep only those paths that begin with v in and end with v out. Keep only those paths that begin with v in and end with v out. If graph has n vertices, then keep only those paths that enter exactly n vertices. If graph has n vertices, then keep only those paths that enter exactly n vertices. Keep only those paths that enter all the vertices at least once. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No” In any paths remain, say “Yes”; otherwise, say “No”

27. 27 A NTIBODY A FFINITY CACCATGTGAC GTGGTACACTG B PMP + Anneal CACCATGTGAC GTGGTACACTG B + CACCATGTGAC GTGGTACACTG B PMP Bind Add oligo with Biotin label Heat and cool Add Paramagnetic-Streptavidin Particles Isolate with Magnet N S

28. 28 Every Vertex V1V1 E 0->1 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 1->2 E 2->3 E 3->4 E 4->5 E 5->6 V3V3 E 0->3 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 3->2 E 2->3 E 3->4 E 4->5 E 5->6

29. 29 Algorithm Generate Random Paths through the graph. Generate Random Paths through the graph. Keep only those paths that begin with v in and end with v out. Keep only those paths that begin with v in and end with v out. If graph has n vertices, then keep only those paths that enter exactly n vertices. If graph has n vertices, then keep only those paths that enter exactly n vertices. Keep only those paths that enter all the vertices at least once. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No” In any paths remain, say “Yes”; otherwise, say “No”

30. 30 Hamiltonian Path V1V1 E 0->1 V0V0 V2V2 V3V3 V4V4 V5V5 V6V6 E 1->2 E 2->3 E 3->4 E 4->5 E 5->6

31. 31 Mismatches

32. 32Errors

33. 33 DNA Word Design Constraints Sequence design should implement the architecture. Sequence design should implement the architecture. Planned Hybridizations Planned Hybridizations Problem Size Problem Size Subsequent Processing Reactions Subsequent Processing Reactions Designed sequences should minimize unplanned “cross-hybridizations.” Designed sequences should minimize unplanned “cross-hybridizations.” Consequences of Bad Designs: Errors and Poor Efficiency Consequences of Bad Designs: Errors and Poor Efficiency

34. 34 DNA Word Design Design problem is hard (NP-Complete). Design problem is hard (NP-Complete). As number of sequences required to represent the problem increases, this constraints increasingly conflicts with the requirement of non-crosshybridization. As number of sequences required to represent the problem increases, this constraints increasingly conflicts with the requirement of non-crosshybridization. How much of DNA sequence space is available for computation and assembly? How much of DNA sequence space is available for computation and assembly?

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37. 37 Library Sizes

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39. 39 Nanotechnology Code

40. 40 First Step

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42. 42 Team Russell Deaton, Weixia Yu, Maryam Nuser, Chris Harris, University of Arkansas, Computer Science and Engineering Russell Deaton, Weixia Yu, Maryam Nuser, Chris Harris, University of Arkansas, Computer Science and Engineering Junghuei Chen, Hong Bi, Yu-Zhen Wang, University of Delaware, Chemistry and Biochemistry Junghuei Chen, Hong Bi, Yu-Zhen Wang, University of Delaware, Chemistry and Biochemistry Jin-Woo Kim, Dylan Carpenter, Ju Seok Lee, University of Arkansas, Biological Engineering Jin-Woo Kim, Dylan Carpenter, Ju Seok Lee, University of Arkansas, Biological Engineering Max Garzon, University of Memphis, Computer Science Max Garzon, University of Memphis, Computer Science Harvey Rubin, University of Pennsylvania, School of Medicine Harvey Rubin, University of Pennsylvania, School of Medicine David Wood, University of Delaware, Computer and Information Science David Wood, University of Delaware, Computer and Information Science

43. 43 Acknowledgement This work was supported by the NSF QuBIC program, award number EIA-0130385 This work was supported by the NSF QuBIC program, award number EIA-0130385