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AQUEOUS COMPUTING - Writing on Molecules -

AQUEOUS COMPUTING - Writing on Molecules -. T. Head, M. Yamamura, and S. Gal Binghamton University. 1. Introduction. The only way to compute with DNA? 1 design sequences for DNA molecules 2 order many custom DNA molecules 3 anneal and filter ( 4 if failure goto 1 ). ↓

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AQUEOUS COMPUTING - Writing on Molecules -

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  1. AQUEOUS COMPUTING - Writing on Molecules - T. Head, M. Yamamura, and S. Gal Binghamton University

  2. 1. Introduction • The only way to compute with DNA? 1 design sequences for DNA molecules 2 order many custom DNA molecules 3 anneal and filter ( 4 if failure goto 1 ) ↓ • Aqueous computing • framework for using molecular memory • laboratory implementation CEC'99

  3. DNA specific subsequence markings on molecules 1. individual access 2. randomize location 3. parallel processing easily separate mix again Molecular Memory Memory LSI HD Address wired grid head pos. Content electronic magnet ic 1. molded together 2. fixed on solid materials 3. serial processing AQUEOUS CEC'99

  4. 2. Mathematical Basis • Common algorithmic problem (CAP) • a description of the pattern of the problem • Aqueous algorithm • a way to use molecular memory CEC'99

  5. Common algorithmic problem • CAP given S: finite set F ⊂2S (the forbidden subsets) find the largest cardinal number n for which there is a subset T of S for which: |T|=n, ∀U∈F U⊂T. • NP-complete problems having the CAP pattern • maximum independent set • minimum vertex cover • Hamiltonian cycles • Boolean satisfiability, etc. CEC'99

  6. Find max # of animals you can keep in one cage? Example • Maximum independent set problem given: G=(V, A) (the arcs are forbidden) find max |T| s.t. T⊂V ,∀x,y∈T, {x,y}∈A CEC'99

  7. Aqueous Algorithm Initialize; For each {s1, s2, ..., sk} in F Do Pour (k) 1: SetToZero( s1 ) 2: SetToZero( s2 ) ... k: SetToZero( sk ) Unite EndFor; MaxCountOfOnes CEC'99

  8. a Pour(2) b c SetToZero(a) SetToZero(b) 011 101 Pour(2) SetToZero(b) SetToZero(c) 001,101 010,100 MaxCountOfOnes: 2 001,101,010,100 Example Initialize: 111 CEC'99

  9. 3. Biomolecular Implementation • DNA modification enzymes • how to write on molecules • DNA plasmid • use of bacteria and blue/white selection CEC'99

  10. Write on molecules • Restriction enzyme • cuts DNA at a specific subsequence (site) 5’-TATCGA-3’ 3’-ATAGCT-5’ ↓ Hind III 5’-T ATCGA-3’ 3’-ATAGC T-5’ • Circular DNA + modification enzymes • Bit =1 (site exists), =0 (no site) CEC'99

  11. Cut/fill/paste 5’-TATCGA-3’ Bit=1, circular 3’-ATAGCT-5’ cut ↓ restriction enzyme 5’-T ATCGA-3’ linear 3’-ATAGC T-5’ fill ↓ DNA polymerase 5’-TATCG ATCGA-3’ 3’-ATAGC TAGCT-5’ paste ↓ DNA ligase 5’-TATCGATCGA-3’ 3’-ATAGCTAGCT-5’ Bit=0, circular CEC'99

  12. ampr b-galactosidase MCS Cloning with DNA plasmid • DNA plasmid • circular, double stranded • set of unique sites • multiple cloning site (MCS) • transform to bacteria • useful genes • antibiotics resistance (ex.ampr) • coloring matters (b-galactosidase) NotI XbaI SpeI BamHI XmaI PstI EcoRI EcoRV HindIII ... 5’-GCGGCCGCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTTATCGAT-3’ 3’-CGCCGGCGACATCTTGATCACCTAGGGGGCCCGACGTCCTTAAGCTATAGTTCGAATAGCTA-5’ CEC'99

  13. Genetic code translation • Genetic code • translated into a series of amino acids by groups of 3 base pairs (codon) • Reading frame • 3 different meanings ex) 5’-GCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATATC A L E L V D P P G C R N S I . . . . . . . . . . . . . . . . . . . . . . . . . . . . (under construction) CEC'99

  14. ↓ • 1st cut/fill/paste +4bp ⇒ reading frame shift → white ↓ • 2nd cut/fill/paste +8bp ⇒ reading frame still shift → white ↓ • 3rd cut/fill/paste +12bp ⇒ readinf frame restored → blue • useful as a debugging tool Blue / white selection • initial DNA plasmid express b-galactosidase gene → blue CEC'99

  15. Blue/white example CEC'99

  16. Preliminary results XbaIBamHIHindIII pBSK GCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTTATCGATACCGTCG A L E L V D P P G C R N S I S S L S I P S [H] GCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTAGCTTATCGATACC A L E L V D P P G C R N S I S S stop [HB] GCTCTAGAACTAGTGGATCGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTAGCTTATCGA A L E L V D R S P G L Q E F D I K L A Y R [HBX] GCTCTAGCTAGAACTAGTGGATCGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTAGCTTA A L A R T S G S I P R A A G I R Y Q A S L sample blue / white accuracy [H] 4 / 40 87% [HB] 3 / 80 96% [HBX] 97 / 17 85% SetToZero Hind III -> BamH I -> Xba I CEC'99

  17. a b c [HB] (+8, white) a=0 (SpeI) b=0 (XhoI) b=0 (XhoI) c=0 (XbaI) 0 +4 +8 +12 mix; +12 & +16 (solution = +12, white) Example under construction CEC'99

  18. 4. Discussion • Advantages as DNA computing • start with one DNA plasmid • no custom DNA for individual problem • amplify in bacteria • blue/white selection as debugging tool • preserving the distribution of DNA plasmids CEC'99

  19. 5. Conclusion • Molecular Memory • Aqueous Algorithm • general framework to use molecular memory • Cut/fill/paste • laboratory implementation • Further issues • scale up & speed up • new algorithm fits bacteria CEC'99

  20. International Connection Binghamton University (USA) Aqueous Computing Leiden University (Netherlands) Tokyo Institute of Technology (Japan) CEC'99

  21. Acknowledgement • Xia Chen & Shalini Aggarwal in S.Gal Laboratory at Binghamton University • NSF CCR-9509831 • DARPA/NSF CCR-9725021 • JSPS-RFTF 96100101 • LCNC at Leiden University CEC'99

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