JSPS Project on Molecular Computing(presentation by Masami Hagiya)

• funded by Japan Society for Promotion of Science

• Research for the Future Program– biocomputing field chaired by Prof. Anzai

• molecular computing

• artificial cell (chemical IC)

• evolutionary computation

• input/output for molecular computing

• complex systems

• October 1996 - March 2001– 60M-80M yen per year

• project leader - Masami Hagiya (Computer Science)

• members– Takashi Yokomori (Computer Science)– Akira Suyama (Biophysics)– Yuzuru Husimi (Biophysics)– Kensaku Sakamoto (Biochemistry)– Shigeyuki Yokoyama (Biochemistry)– Masayuki Yamamura (Computer Science)– Masanori Arita (Genome Informatics)

JSPS Project on Molecular Computing

• Kensaku Sakamoto, Shigeyuki Yokoyama, and Masami Hagiya– Whiplash PCR

– Hairpin Engine

• Masami Hagiya– VNA --- a simulator for DNA reactions

• Takashi Yokomori – Formal Models of DNA Computing

• Akira Suyama– Solid-based DNA Computing

– Dynamic Programming DNA Computers

• Yuzuru Husimi– Evolutionary Reactor Based on 3SR

• Masayuki Yamamura– Aqueous Computing (with Tom Head)

Some Achievements

DNA Computing• Adleman-Lipton Paradigm ＝ massive parallelism

– random generation by assembly– selection by molecular biology experiments

large size ⇒ increase yield and decrease error

• one-pot reaction ⇒autonomous computation ・ self-organization– DNA tiles by Winfree– DNA automata by Hagiya et al.– DNA boolean circuits by Ogihara and Ray

• theoretical analyses– complexity– formal languages

⇒ combinatorial optimization

Brute Force v.s. Dynamic ProgrammingDNA Computers

Solution

GeneratingWHOLE

solution spaceAT ONCE

GeneratingWHOLE

solution spaceAT ONCE

SelectionSelection

Brute ForceLarge pool size

Low reaction rate

Solution

GeneratingPARTIAL

solution spaceSTEP BY STEP

GeneratingPARTIAL

solution spaceSTEP BY STEP

SelectionSelection

Dynamic Programmingsmall pool size

High reaction rate

3-CNF SAT Solution on DP DNA Computer

}{

YES

)()(

)()(

)()(

)()(

)()(

clauses10variables,4

4321

432432

432431

421431

321321

321321

FFTT XXXX

xxxxxx

xxxxxx

xxxxxx

xxxxxx

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:Solution

:Problem

∫↓∫↓⎮∫↓∫⎮∫∫⎮↓∫∫↓⎮↓∫∫↓⎮↓∫↓∫

⎮↓∫↓∫↓⎮↓∫∫↓⎮∫↓∫⎮∫∫

Basic Operations forDynamic Programming DNA Computers

get (T, +s), get (T, -s)get DNA molecules with a subsequence s (without s) in a tube T

append (T, s, e)append a subsequence s at the end of DNA molecules with a splint e in a tube T

merge (T, T1, T2, …, Tn)merge DNA molecules in tubes T1, T2, …, Tn into a tube T

amplify(T, T1, T2, …, Tn)amplify DNA molecules in a tube T and divide them into tubes T1, T2, …, and Tn

detect(T)detect DNA molecule in a tube T

Implementation of Basic Operations

annealingand

ligation s

s

immobilizationand

cold wash

s

s

hot wash

s

Taq DNA ligase

get (T, +s), get (T, -s)

s

s

annealing

immobilization

cold wash

hot washs get (T, +s)

get (T, -s)

s

s

amplify (T, T1, T2, …Tn)

PCR

immobilizationand

cold wash

hot washand

divide

annealing T

T1, T2, …Tn

append (T, s, e)

e

e

DP algorithm for 3CNF-SAT on DNA Computers

end

end

end

end

thenif

end

thenif

dotofor

dotofor

begin

procedure

));((detectoutput

);',,(amplify);',,(amplify

);,,(append);,,(append

);,,(getuvsat

''

);,,(getuvsat

''

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);',',(merge);,,(merge

3

);'(input};,{');(input};,{

);'(input};,{');(input};,{

),,,...,,,(sat3dna

/1

/1

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221212221212

111

n

Fk

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Fk

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FTk

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T

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kj

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w

kj

Fk

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FFFFTFFFFFTF

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mmm

T

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vuTT

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nk

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wvuwvu

−−

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==

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===

==

end

begin

procedure

);(output

);,,(merge

);,(get

);,'(get

);,(get');,(get

);(input

),,(getuvsat

T

Tv

Tu

T

Tv

Fu

Tv

Fu

Fu

Fu

Tu

Fu

Tu

Tu

T

TTT

XTT

XTT

XTTXTT

T

vuT

+=

+=

−=+=

mergeget3

amplify)2

append2merge2(

operationsofNumber

↔+↔+

↔+↔+↔↔

mm

n

An Amount of DNA for ComputationBrute Force v.s. Dynamic Programming

100 variable 3-CNF SAT

Adleman-Lipton’s

Brute Force

DNA Computers

2x1012 g of dsDNA

1x1012 g of ssDNA

Dynamic Programming

DNA Computers

2x10-3 g of dsDNA

(1x10-3 g of ssDNA)

Autonomous ComputationSelf-Organization

• computational power of assembly (Winfree)– string ― regular– tree ― context-free– tile ― universal

• autonomous state transition (Hagiya et al.)– Whiplash PCR

• applications ― not only combinatorial optimization, but also

– nanotechnology– genetic analysis

x B A xC

Bx

ab

Whiplash PCR

x B A xC

B

Whiplash PCR

x B A x C B x

a

Whiplash PCR

x B A x C B x

a

bc

Whiplash PCR

Encoding

VNA: Simulator for Virtual DNA• a concrete computational model based on

assembly and state transition

• abstract, but sufficiently physicalbridging gaps between abstract models and real reactions

molecule ― hybrid of virtual strandsabcd || CDEF

• reactions– hybridization assembly– denaturation decomposition– restriction decomposition– ligation state transition– self-hybridization state transition– extension state transition

VNA (cont.)• purposes

– verify feasibility of algorithms for DNA computing– verify validity of molecular biology experiments

(e.g., PCR experiments)– parameter fitting in molecular biology experiments

• examples– Ogihara and Ray’s computation of boolean circuits– Winfree’s construction of double-crossover units– PCR experiments

• implementation– Java executable as an applet⇒

VNA (cont.)

• methods– combinatorial enumeration– continuous simulation (diff. eq.)

• avoiding combinatorial explosion

contributions in simulation technology– threshold– stochastic

• parameter fitting by GA– optimizing amplification in PCR experiments

unify

Goals of Molecular Computing― concluding remark

• analysis of computational power of biomolecules– understanding life

• origin of life

• wet artificial life

– engineering applications• combinatorial optimization

• nanotechnology

• genetic analysis

• new computational models ・ simulation technology

digital ⇒ analog ⇒ physical