1. Introduction to DNA Computing Russell Deaton Elec. & Comp. Engr. The University of Memphis Memphis, TN 38152 rjdeaton@memphis.edu Junghuei Chen Department of Chem & Biochem University of Delaware Newark, DE 19716 Junghuei@udel.edu

2. What is DNA Computing (DNAC) ? The use of biological molecules, primarily DNA, DNA analogs, and RNA, for computational purposes.

3. Why Nucleic Acids? Density (Adleman, Baum): –DNA: 1 bit per nm 3, 10 20 molecules –Video: 1 bit per 10 12 nm 3 Efficiency (Adleman) –DNA: 10 19 ops / J –Supercomputer: 10 9 ops / J Speed (Adleman): –DNA: 10 14 ops per s –Supercomputer: 10 12 ops per s

4. What makes DNAC possible? Great advances in molecular biology –PCR (Polymerase Chain Reaction) –DNA Microarrays –New enzymes and proteins –Better understanding of biological molecules Ability to produce massive numbers of DNA molecules with specified sequence and size DNA molecules interact through template matching reactions

5. What are the basics from molecular biology that I need to know to understand DNA computing?

6. 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’

7. I NTER-STRAND H YDROGEN B ONDING AdenineThymine to Sugar-Phosphate Backbone to Sugar-Phosphate Backbone (+)(-) (+)(-) Hydrogen Bond GuanineCytosine to Sugar-Phosphate Backbone to Sugar-Phosphate Backbone (-) (+) (-) (+) (-)

8. S TRAND H YBRIDIZATION A B a b A B a b b B a A HEAT COOL b a A B OR 100° C

9. DNA L IGATION  ’’ ’’ ’’ ’’ Ligase Joins 5' phosphate to 3' hydroxyl ’’ ’’  

10. R ESTRICTION E NDONUCLEASES EcoRI HindIII AluI HaeIII - OH 3’ 5’ P - - P 5’ 3’ OH -

11. DNA Polymerase

13. DNA Sequencing

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

15. 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

16. POLYMERASE CHAIN REACTION

19. What is a the typical methodology? Encoding: Map problem instance onto set of biological molecules and molecular biology protocols Molecular Operations: Let molecules react to form potential solutions Extraction/Detection: Use protocols to extract result in molecular form

20. What is an example? “Molecular Computation of Solutions to Combinatorial Problems” Adleman, Science, v. 266, p. 1021.

22. Algorithm Generate Random Paths through the graph. 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. Keep only those paths that enter all the vertices at least once. In any paths remain, say “Yes”; otherwise, say “No”

23. Encoding 0 1 2 ‘GCATGGCC ‘AGCTTAGG ‘ATGGCATG CCGGTCGA’ CCGGTACC’ ‘GCATGGCCAGCTTAGG CCGGTCGA’ ‘GCATGGCCATGGCATG CCGGTACC’ 0021

25. What are the success stories? Self-Assembling Computations Demonstrated (Winfree and Seeman) New Approaches and Protocols Developed –Surface-based (Wisconsin-Madison, Dimacs II) –Evolutionary Approaches (Wood and Chen, Gecco-99, DNA-5) How do cells and nature compute? (Kari and Landweber, Dimacs IV)

26. Source: http://seemanlab4.chem.nyu.edu/

27. Source: Winfree, DIMACS IV

29. Source: http://corninfo.chem.wisc.edu/writings/dnatalk/dna01.html

31. Source: http://www.princeton.edu/~lfl/washpost.html

32. What are the challenges? Error: Molecular operations are not perfect. Reversible and Irreversible Error Efficiency: How many molecules contribute? Encoding problem in molecules is difficult. Scaling to larger problems Applications

33. Mismatches

34. DNA Word Design Design of DNA Sequences that hybridize as planned (that is, minimize mismatches) Reliability: False Positives and Negatives Efficiency: Hybridizations that Contribute to Solution Hybridizations are Templates for Subsequent Enzymatic Steps

35. DNA Word Design Minimum Distance Codes to Prevent Hybridization Error Distance Measure –Combinatoric (Hamming) –Energetic (Base Stacking Energy) Design DNA Words with Evolutionary Algorithms Good Codes Achievable

36. Code Word Hybridization Code Word Hybridization

37. Base Stacking

39. What are the possible applications? DNAC and Conventional Computers DNAC and Evolutionary Computation DNAC and Biotechnology

40. DNAC and Electronic Computing Solution versus solid state Individual molecules versus ensembles of charge carriers The importance of shape in biological molecules Programmability/Evolvability Trade-off (Conrad)

41. Edna Electronic DNA Virtual Test Tube for Design and Simulation of DNA Computations Molecules as Cellular Automata Solve Adleman and Other Problems Distributed Edna to Solve Large Problems New Paradigm

43. In Vitro Evolutionary Computation Randomness and Uncertainty Inherent in Biomolecular Reactions Never Level of Control like EE over Solid State Devices Use Nature’s ToolBox: Enzymes, Reaction/Diffusion, Adaptability, and Robustness Evolved, Not Designed

46. DNAC and Biotechnology “Computationally Inspired Biotechnology” DNA 2 DNA “killer app” Automation of protocols DNA Word Design (Gene Expression Chips) Exquisite Detection of Biomaterials Bio-engineered Materials

47. What developments can we expect in the near-term (1999)? Increased use of molecules other than DNA Evolutionary approaches Continued impact by advances in molecular biology Some impact on molecular biology by DNA computation Increased error avoidance and detection

48. What are the long-term prospects? Cross-fertilization among evolutionary computing, DNA computing, molecular biology, and computational biology Niche uses of DNA computers for problems that are difficult for electronic computers

49. Where can I learn more? Web Sites: http://www.wi.leidenuniv.nl/~jdassen/dna.html http://dope.caltech.edu/winfree/DNA.html http://www.msci.memphis.edu/~garzonm/bmc.html (Conrad) http://www.cs.wayne.edu/biolab/index.html DIMACS Proceedings: DNA Based Computers I (#27), II (#44), III (#48), IV (Special Issue of Biosystems), V (MIT, June 1999), VI (Leiden, June 2000) Other: Genetic Programming 1 (Stanford, 1997), Genetic Programming 2 (Wisconsin-Madison, 1998), GECCO-1999, IEEE International Conference on Evolutionary Computation (Indianapolis, 1997) G. Paun (ed.), Computing with Biomolecules: Theory and Experiment, Springer-Verlag, Singapore 1998. “DNA Computing: A Review,” Fundamenta Informaticae, vol. 35, pp. 231-245. M. H. Garzon and R. J. Deaton, “Biomolecular Computing and Programming,” IEEE Transactions on Evolutionary Computation, vol. 3, pp. 236-250, 1999.