Horn Clause Computation by Self-Assembly of DNA Molecules Hiroki Uejima Masami Hagiya Satoshi...

Horn Clause Computation by Self-Assembly of DNA Molecules

Hiroki UejimaMasami Hagiya

Satoshi Kobayashi

Previous Works(SIMD Type Computation) Solution to HPP by Adleman (1994)

For a 7-vertex directed graph Adleman-Lipton paradigm (1995)

Solution candidates are randomly generated. Real solutions are selected from among the genera

ted candidates. Applying a single operation to multiple molecul

es expressing data at once.

Previous Works(Computational Power/Model) The correspondence between forms of DNA m

olecule and computational power based on formal languages.

Various computational models Branching program Turing machine Boolean circuit Random Access Memory Horn clause computation (Kobayashi)

Horn Clause Computation Model by Kobayashi Each molecule corresponds to a

Horn clause. One step of derivation is realized

by one biological operation. SIMD type computation The number of operations is

proportional to the size of problem.

Previous Works(Autonomous Computation) Computation proceeds

autonomously by self-assembly of DNA.

Possible to keep the number of operations constant.

Computation with DNA tiles A simulation of 1-D cellular automata String tiling

Structure of DNA Tile

X

X

X

Y

Y

Z

Z

Z

Y

W

W

W

cf. Winfree’s DNA Tile

Contribution of This Work A Proposal and an analysis of a

new model of DNA computation Based on Horn clause computation Autonomous by self-assembly of DNA

molecules A theoretical research on a

possibility of molecular computation.

Outline of The Algorithm To generate ground Horn clauses by vari

able substitution, using string tiles. The ground clauses are generated randomly

by self-assembly of DNA. This phase proceeds autonomously.

To make a deduction on the ground clauses. This phase also proceeds autonomously.

Horn Clause Usedin This Algorithm A term in a rule is the form f1(…fn(X)…). The arity of a predicate is at most 2. The arity of a function is 1 The variable of the 1st argument of an at

om is X, the 2nd is Y. A fact contains no variables.

Correspondence between DNA and Horn Clause DNA molecule expressing Horn

clause Fact molecule Rule molecule

~Q ~R

P

Q

~Q

P

P ← Q, RP ← QQ

sticky end

The Resolution Principleby Self-Assembly of DNA

~Q

~R

P

Q

~S

~T

P ← Q, R

Q ← S, T

P ← Q, R　　 Q ← S, T

P ← S, T, R

Result Detection To put query molecules in To ligate molecules To detect a circular form

molecule~P P

The query molecule to

detect the fact P

Start!

Self-assembly

Self-assembly

Putting query molecules in

Query molecule

Ligation

Another example of circular molecule

Computational Complexity Time complexity

(The number of operations): constant Space complexity

(The minimum number of molecules to derive a fact): O(2n)

What’s String Tile Proposed by Winfree et al. (2000) String tiling is the collapse of multi-layer

assemblies into simpler superstructures. A string tile has a directed graph inside, t

he edges of the graph corresponds to DNA strands.

The graphs are connected with each other by hybridization of tiles.

Variable Substitutionby Self-Assembly of String Tile

P(f(X), Y) ← Q(X, g(Y))a / Y g(X) / X b / X

P(f(g(b)), a) ← Q(g(b), g(a))

Substitution tile Substitution tileSeed tile

A(f(X),Y) ← B(X, g(Y)), C(X, Y)

g(X) / X b / Xa / Y

A(f(g(b)), a) ← B(g(b), g(a)), C(g(b), a)

A(f(g(b)), a) ← B(g(b), g(a)), C(g(b), a)

A(f(g(b)), a) ← B(g(b), g(a)), C(g(b), a)

B(g(b), g(a))

C(g(b), a)

A(f(g(b)), a)

A(f(g(b)), a) ← B(g(b), g(a)), C(g(b), a)

A(f(g(b)), a)

B(g(b), g(a))

C(g(b), a)

A(f(g(b)), a) ← B(g(b), g(a)), C(g(b), a)

NTM Simulation by Horn Clause Computation

Configuration is expressed by fact. Ss(ft(-1)(ft(-2)(fb(a1))), ft(0)(ft(1)(fb(fb(a2)))))

Transition rule is expressed by rule. Ss’(X, ft(-1)(ft’(0)(Y))) ← Ss(ft(-1)(X), ft(0)(Y)) Ss’(ft’(0)(X), Y) ← Ss(X, ft(0)(Y))

b t(-2) t(-1) t(0) t(1) b b

s

Features of Our Model Autonomous computation keeps

the number of operations constant. Our model is equivalent to non-

deterministic Turing machine. Variable substitution phase are

separated from deduction phase completely.

Advantage of Our Model Close relation to high-level

programming language PROLOG (Horn clause computation)

More suitable for expressing complex algorithms than other models.

Small number of operations(Autonomous computation)

Weak Point of Our Model Error-prone deduction

Term encoding has problem Too long sticky end Biased deduction

Estimation of complexity is not appropriate. Time complexity: Time to reach equilibrium is more

appropriate than the number of operations. Space complexity: More molecules will be required

because multiple proof trees are generated. 3-D conformation of proof tree molecule

Future Works Thermodynamic/kinetic analysis of

autonomous DNA computation Optimization of parameters

according to the analysis Temperature Salt concentration

Analysis of DNA computation as probabilistic algorithm