AQUEOUS COMPUTING- Writing on Molecules -

T. Head, M. Yamamura, and S. GalBinghamton University

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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

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Molecular Memory

MemoryLSI HD

Address wired grid head pos.

Content electronic magnetic

1. molded together

2. fixed on solid materials

3. serial processing

AQUEOUS

DNA

specific subsequence

markings on molecules

1. individual access

2. randomize location

3. parallel processing

easily separate

mix again

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2. Mathematical Basis

Common algorithmic problem (CAP)– a description of the pattern of the problem

Aqueous algorithm– a way to use molecular memory

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Common algorithmic problem CAP

given S: finite set

F 2⊂ S (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.

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Example

Maximum independent set problemgiven: G=(V, A) (the arcs are forbidden)

find max |T| s.t. T⊂V , x,y T, {x,y} A∀ ∈ ∈

Find max # of animals you can keep in one cage?

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Aqueous Algorithm

Initialize;For each {s1, s2, ..., sk} in F DoPour (k)

1: SetToZero( s1 )2: SetToZero( s2 )

...k: SetToZero( sk )

UniteEndFor;MaxCountOfOnes

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Pour(2)

SetToZero(b) SetToZero(c)001,101 010,100

SetToZero(a) SetToZero(b)011 101

Pour(2)

ExampleInitialize: 111

a

bc

MaxCountOfOnes: 2001,101,010,100

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3. Biomolecular Implementation

DNA modification enzymes– how to write on molecules

DNA plasmid– use of bacteria and blue/white selection

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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)

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Cut/fill/paste

5’-TATCGA-3’ Bit=1, circular3’-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

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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)

amp

r

-ga

lact

osid

ase

MCS

NotI XbaI SpeI BamHI XmaI PstI EcoRI EcoRV HindIII ...5’-GCGGCCGCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATATCAAGCTTATCGAT-3’3’-CGCCGGCGACATCTTGATCACCTAGGGGGCCCGACGTCCTTAAGCTATAGTTCGAATAGCTA-5’

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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’-GCTCTAGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGATA

TC A L E L V D P P G C R N S I . . . . . . . . . . . . . . . . . . . . . . . . . . . .

(under construction)

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Blue / white selection initial DNA plasmid

express -galactosidase gene → blue↓

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

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Blue/white example

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Preliminary results XbaI BamHI HindIII

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%

SetToZeroHind III

-> BamH I -> Xba I

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Example

[HB] (+8, white)

a=0(SpeI)

b=0(XhoI)

b=0(XhoI)

c=0(XbaI)

mix; +12 & +16(solution = +12, white)

a

bc

0 +4 +8 +12

under construction

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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

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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

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International Connection

BinghamtonUniversity

(USA)

LeidenUniversity

(Netherlands)

Tokyo Institute ofTechnology

(Japan)

Aqueous Computing

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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