1. Introduction to DNA Computing Russell Deaton Electrical Engineering The University of Memphis Memphis, TN 38152 rjdeaton@memphis.edu Stephen A. Karl Department of Biology University of South Florida Tampa, FL 33620 karl@chuma.cas.usf.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) –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. G EL E LECTROPHORESIS - SIZE SORTING Buffer Gel Electrode Samples Faster Slower

12. 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

13. P OLYMERASE C HAIN R EACTION A MPLIFICATION Cycle 1 Cycle 2 Cycle 3 - 35... 3’ 5’ 3’ 5’ 3’ 5’ 3’ 5’ 3’ 5’ 3’ Heat 95° C Cool 55° C Synthesize 72 ° C

14. 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

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

18. What are the success stories? Self-Assembling Computations Demonstrated (Winfree and Seeman) New Approaches and Protocols Developed –Surface-based (Wisconsin-Madison, Dimacs II) –PCR-based (Hagiya et al., Dimacs III) –Parallel Overlap Assembly (Kaplan et al., Dimacs II) –DNA Addition (Guarnieri and Bancroft, Dimacs II)

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

20. Source: Winfree, DIMACS IV

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

22. Source: Hagiya, DIMACS III

23. 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

24. What are the challenges for Computer Science? Discover problems DNA Computers are good at –Messy reactions as positive –Evolvable, not programmable Characterize complexity for DNA computations with bounded resources New notions of what a “computation” is?

25. What are the challenges for molecular biology? Develop computation-specific protocols Better understanding of basic mechanisms and properties Better characterization of processes Measures of reliability and efficiency Advanced understanding of biomolecules other than DNA and RNA

26. How does DNAC relate to 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)

27. How does DNAC relate to evolutionary computing? DNA is at the core of biological evolution. Evolutionary Computing implementation in vitro with DNAC Enzymes changing the Sequence Use DNAC “errors” for similarity and fault- tolerance in vitro evolution and DNAC

30. How does DNAC relate to computational biology? Mirror images of each other Computational biology interested in applying CS to solve biological problems DNAC interested in applying biology to solve computational problems Use DNAC to solve computational biology problems

31. How does DNAC relate to living systems? Kari and Landweber (Dimacs IV) How do cells and nature compute? Thesis: Ciliates compute a difficult HP problem in gene unscrambling. Similarities to Adleman’s Path Finding Problem in the Cell

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

33. What advances in molecular biology might benefit DNAC? Detergents Synthetic bases Error-prone PCR New enzymes Designer molecules Charge Transfer along DNA Improved separation techniques

34. What advances in DNAC might benefit molecular biology? Lipton and Landweber (Dimacs III)s DNA 2 DNA “killer app” Automation of protocols Better estimation of error rates 3-dimensional structure analysis Increased fidelity and efficiency of techniques

35. What developments can we expect in the near-term? 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

36. What are the long-term prospects? Cross-fertilization among evolutionary computing, DNA computing, molecular biology, and computation biology Niche uses of DNA computers for problems that are difficult for electronic computers Increased movement into exploring the connection between life and computation?

37. 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) Other: Genetic Programming 1 (Stanford, 1997), Genetic Programming 2 (Wisconsin-Madison, 1998), 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, 35, 231-245.