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DNA Computing Tutorial

DNA Computing Tutorial Biointelligence Lab. School of Compute Science and Engineering, Seoul National University 2002. 3. 13 Outline Molecular Intelligence DNA computing operators Introduction of DNA computing Examples The first DNA computing approach Molecular theorem prover

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DNA Computing Tutorial

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  1. DNA Computing Tutorial Biointelligence Lab. School of Compute Science and Engineering, Seoul National University 2002. 3. 13 (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  2. Outline • Molecular Intelligence • DNA computing operators • Introduction of DNA computing • Examples • The first DNA computing approach • Molecular theorem prover • The difficulties of DNA computer • DNA computing applications • Conclusion (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  3. Molecular Intelligence • Solving artificial intelligence problems using molecular computing technology • Computing model: evolutionary computing • Computing devices: biomolecules (DNA) • Learning and inference using biomolecular computing • Biochemical reaction • Massively parallel • Micro or nanoscale • Storage capacity • Bio-compatible Artificial Intelligence Molecular Computing Molecular Intelligence

  4. BT IT Biocomputing vs. Bioinformatics Bioinformatics Biocomputing

  5. What is DNA Computing? The use of biological molecules, primarily DNA, DNA analogs, and RNA, for computation purpose. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  6. DNA Computing 011001101010001 ATGCTCGAAGCT

  7. A different approach • What DNA computing is: • a completely new method among a few others (e.g., quantum computing) of general computation alternative to electronic/semiconductor technology • uses biochemical processes based on DNA • What DNA computing isn’t: • not to confuse with bio-computing which applies biological laws (evolution, selection) to computer algorithm design. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  8. Why try new stuff ? • Will need a dramatically new technology to overcome some CMOS limitations and offer new opportunity. • Certain types of problems (learning, pattern recognition, fault-tolerant system, large set search algorithms) are intrinsically very difficult to solve even with fast evolution of CMOS. • Hope of achieving massive parallelism. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  9. Why try DNA Computing? • 6.022  1023molecules / mole • Massively Parallel Search of All Possibilities • Desktop: 109 operations / sec • Supercomputer: 1012 operations / sec • 1 mmol of DNA: 1026 reactions • Favorable Energetics: Gibb’s Free Energy • 1 J for 2  1019 operations • Storage Capacity: 1 bit per cubic nanometer (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  10. Why try DNA Computing? • The fastest supercomputer vs. DNA computer • 106 op/sec vs. 1014 op/sec • 109 op/J vs. 1019 op/J (in ligation step) • 1bit per 1012 nm3 vs. 1 bit per 1 nm3 (video tape vs. molecules) (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  11. Known CMOS limitations Relative Fab Cost 140 nm Gate length 16 80 nm 4.0 4 60 nm parameters approach molecule size Inter-metal Dielectric K 2.7 45 nm 1.6-2.2 1 1.6-2.2 0.25 <1.5 2011 2002 2005 2008 1999 Source: Texas Instruments and ITRS IC Design Technology Working Group (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  12. Future technology enablers True neural computing Bio-electric computers 1e6-1e7 x lower power for lifetime batteries Quantum computer, molecular electronics This talk Smart lab-on-chip, plastic/printed ICs, self-assembly Full motion mobile video/office Vertical/3D CMOS, Micro-wireless nets, Integrated optics Wearable communications, wireless remote medicine, ‘hardware over internet’ ! Pervasive voice recognition, “smart” transportation Metal gates, Hi-k/metal oxides, Lo-k with Cu, SOI +2 +4 +6 +8 +10 +12 Now Source: Motorola, Inc, 2000

  13. Historical timeline Research 1950’s … 1994 1995 2000 2005 R.Feynman’s paper on sub microscopic computers L.Adleman solves Hamiltonian path problem using DNA. Field started D.Boneh paper on breaking DES with DNA Lucent builds DNA “motor” DNA computer architecture ? Commercial 2015 1970’s … 1996 2000 DNA used in bio application Human Genome Sequenced Affymetrix sells GeneChip DNA analyzer Commercial computer ? (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  14. What’s up to date • Research still in very early stages but promise is big • First groundbreaking work by Adleman at USC only 6 years ago • No commercialization in sight within ~10 years • Main work on settling for a logic abstraction model, ways of using DNA to compute • Research sponsored by universities (Princeton, MIT, USC, Rutgers, etc) and NEC, Lucent Bell Labs, Telcordia, IBM • One way or another, DNA computing will have a significant impact (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  15. DNA Computers vs. Conventional Computers (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  16. DNA Computing takes advantage of • Our ability to produce massive numbers of DNA molecules with specific properties (size, sequence) • The natural proclivity of specific DNA molecules to chemically interact according to defined rules to produce new molecules • Laboratory techniques that allow the isolation/identification of product molecules with specific properties • PCR, Ligation, Gel Electrophoresis, etc. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  17. DNA Computing Operator (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  18. genes nucleus Cell chromosome Basic building blocks • Found in every cell’s nucleus, genes consist of tight coils of DNA’s double helix • Number of genes/length of DNA depends on species DNA

  19. DNA Structure Watson-Crick complement

  20. Number System (Base 4): Complement Nucleotide Nucleotide A T C G DNA binding process DNA Memory A string composed of a series of four types of units (nucleotides), DNA may be viewed as logic memory or gate. Two strings of DNA are bonded by paired nucleotides A-C and C-G which may be considered as complements. Example: Number TTACAG has a complement AATGTC

  21. a t c g g t c a t a g c a c t 0 0 0 DNA memory strands 1 0 1 t a g c c c g t g a t c a t a a t c g g DNA Memory Writing : make DNA sequences Reading : hybridization and readout (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  22. DNA Operators • The bio-lab technology. • Hybridization • Ligation • Polymerase Chain Reaction (PCR) • Gel electrophoresis • Affinity separation (Bead) • Enzymes: restriction enzyme… (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  23. Hybridization & Ligation • Hybridization • base-pairing between two complementary single-strand molecules to form a double stranded DNA molecule • Ligation • Joining DNA molecules together • Solution generation step! (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  24. DNA Hybridization & Ligation AGCTTAGGATGGCATGG AATCCGATGCATGGC + CGTACCTTAGGCT Hybridization AGCTTAGGATGGCATGG AATCCGATGCATGGC CGTACCTTAGGCT Ligation AGCTTAGGATGGCATGGAATCCGATGCATGGC CGTACCTTAGGCT Dehybridization AGCTTAGGATGGCATGGAATCCGATGCATGGC + CGTACCTTAGGCT (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  25. PCR • Polymerase Chain Reaction • Amplifies (produces identical copies of) selected dsDNA molecules. • Make 2ncopies (n : number of iteration) • Solution filtering or amplification step! (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  26. PCR (Polymerase Chain Reaction) (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  27. Gel electrophoresis • Molecular size fraction technique • Detect the specific DNA Bead Separation Solution detection or filtering step! (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  28. Gel Electrophoresis Gel image

  29. Bead Separation Magnetic Beads Magnet Complementary

  30. Restriction enzyme • Cut the specific DNA site. • Solution detection or filtering step! A A G C T T T T C G A A OH 3’ 5’ P A A C G T T EcoRI T T C G A A P 5’ 3’ OH (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  31. The First DNA Computing Method L. M. Adleman, Molecular Computation of Solutions to Combinatorial Problems, Science, 266:1021-1024, 1994 (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  32. First special DNA computer • Special problem: given N points, find a path visiting each and every point only once, and starting and ending at a given locations. (Hamiltonian path problem) • Solved with a DNA computer by Leonard Adleman in 1994 for N=6 • Basic approach: code each point as an 8 unit DNA string, code each possible path, allow DNA bonding, suppress DNA with incorrect start/end points. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  33. 4 3 1 0 6 2 5 Hamiltonian Path Problem The Hamiltoian path problem: as the number of cities grows, even supercomputers have difficulty finding the path. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  34. Adleman’s Molecular Computer: A Brute Force Method Each city (vertex) is represented by a different sequence of nucleotides (6 here, but Adleman used 20). A DNA linker (edge) joining two city (vertex) strands. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  35. Encoding (Basic Concept) 4 3 1 0 AGCT TAGG P1A P1B 6 2 5 1 ATCC TACC ATCC GCTA P1B P2A P1B P3A 3 2 ATGG CATG CGAT CGAA P2A P2B P3A P3B (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  36. Procedure Generate random paths through the graph Hybridization & Ligation PCR with vin and vout Keep only those paths that begin with vin and end with vout If the graph has n vertices, then keep only those paths that enter exactly n vertices Gel electrophoresis Keep only those paths that enter all of the vertices of the graph at least once. Antibody bead separation With vi If any paths remain, say “Yes”; otherwise, say “No.” Gel electrophoresis & Sequecing

  37. Vertex 1 Vertex 2 ATGGCATG AGCTTAGG ATCCTACC 16 bp 56 bp Edge 12 Step 4 : Gel Electrophoresis AGCTTAGG ATGGCATG ATCC TACC Step 1 : Hybridization AGCTTAGGATGGCATGGAATCCGA… TCGAATCC AGCTTAGG ATGGCATG ATCCTACC Bead for vertex 1 Step 2 : Ligation Step 5 : Magnetic Bead Affinity Separation Vertex 4 Vertex 1 AGCTTAGGATGGCATGGAATCCGATGCATGGC TCGAATCC ACGTACCG Step 3 : PCR (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  38. Node 0 : ACG Node 3 : TAA Node 1 : CGA Node 4 : ATG Node 2 : GCA Node 5 : TGC Node 6 : CGT HPP Encoding Ligation TAAACG TGC 4 3 ... ATG ... 1 ATGTGCTAACGAACG CGA 0 ACG GCA TAA ACGCGAGCATAAATGTGCACGCGT ... ... ... 6 ... ... ... CGT ... ... TAAACGGCAACG ... ... ... ... 2 5 ... ACGCGAGCATAAATGTGCCGT ... ACGCGAGCATAAATGCGATGCACGCGT ... CGACGTAGCCGT ... ... CGACGT ... ... ... ... ... PCR (Polymerase Chain Reaction) ACGCGAGCATAAATGTGCCGT ACGGCATAAATGTGCACGCGT Gel Electrophoresis ACGCGAGCATAAATGCGATGCCGT Solution Affinity Column 4 3 Decoding ACGCGTAGCCGT 1 ... 0 ACGCGAGCATAAATGTGCCGT ... ... 6 ... ACGCGAGCATAAATGTGCACGCGT ... ACGCGAGCATAAATGTGCCGT ACGCGT 2 5 ... (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/ ... ... ... ... ACGCGAGCATAAATGCGATGCACGCGT

  39. DNA finds a solution! A Hamiltonian path with all vertices included is isolated and recovered (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  40. Another Practical Example Molecular theorem prover (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  41. Molecular Theorem Prover • Resolution refutation method • Problem under consideration: • Turn into , add R as R is true! (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  42. ¬S ¬T Q ¬Q ¬P R ¬S ¬T Q ¬Q ¬P R ¬S ¬T Q ¬Q ¬P R Molecular Theorem Prover(Abstract Implementation) P ¬R S T P ¬R S T P ¬R S T (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  43. Experimental Procedure Step 1. DNA Sequence Design Step 2. DNA Synthesis Step 3. Chemical Reaction in a Test Tube • Hybridization • Ligation Step 4. Detection of Solutions • PCR • Gel Electrophresis (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  44. Sequence Design ¬Q ¬P  R : 5’3’ Q  ¬T  ¬S : 3’ 5’ S : 5’ 3’ T : 3’ 5’ P : 5’ 3’ R : 5’ 3’ ¬R : 3’ 5’ CGT ACG TAC GCT GAA CTG CCT TGC GTT GAC TGC GTT CAT TGT ATG TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT AAG CAG TAG CGA CCA ATT GAC GCA AAT TGA GTC AAC GCA AGG CAG TGC GTT CAT TGT ATG CAT ACA ATG AAC GCA (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  45. Reaction in a Test Tube Q  ¬T  ¬S P T R  ¬P¬Q S ¬R (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  46. Q  ¬T  ¬S TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT P AGT TAA ACG CAG TTA ¬R GTC AAC GCA AGG CAG T AGT TAA ACG CAG TTA ¬R P Q  ¬T  ¬S P CAT ACA ATG AAC GCA ¬R T Q  ¬T  ¬S ACC AGC GAT GAC GAA GTC AAC GCA AGG CAG GTC AAC GCA AGG CAG TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT CAT ACA ATG AAC GCA S CAT ACA ATG AAC GCA TTC AGC GTA CGT ACG TCA ATT TGC GTC AAT TGG TCG CTA CTG CTT GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC ACC AGC GAT GAC GAA AGT TAA ACG CAG TTA GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC R  ¬P¬Q T S R  ¬P¬Q ACC AGC GAT GAC GAA GTA TGT TAC TTG CGT CAG TTG CGT TCC GTC AAG TCG CAT GCA TGC R  ¬P¬Q S Hybridization and Ligation (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  47. Results II. Denaturation ( 95°C 10 min) IV. Polyacrylamide gel Electrophoresis(20%) ( PAGE ) V. Detection of solution : 75bp ds DNA I. MIX 100pmol/each  Total 20 ul 1 2 3 4 6 5 200 bp III. Annealing 95°C 1 min  15 °C : 1°C down/min 20 bp Molecular Theorem Prover 20 bp DMA marker (Talara) Mixture Reaction • Experimental Steps (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  48. The difficulties of DNA computer (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  49. The Reality of DNA based computation • Symmetry makes control difficult. • Importance of Non-Watson-Crick Pairing. • Importance of hybridization protocols. • Importance of concentration and environment. • Fidelity of ligation is high, but NOT perfect. • DNA molecules breath. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

  50. The Reality of DNA based computation • Affinity binding is a filtration process. • Complex ligation produces complex products. • Restriction enzymes produces partial digestion products. • Base stacking is often determining interaction. • DNA is a physical chemical system. (C) 2002, SNU Biointelligence Lab, http://bi.snu.ac.kr/

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