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An evolutionary Monte Carlo algorithm for predicting DNA hybridization. Joon Shik Kim et al. (2008) 11.05.06.(Fri) Computational Modeling of Intelligence Joon Shik Kim. Neuron and Analog Computing. Analog Computing. Neuron. Spin glass system. Spin Glass. < S >= Tanh(J<S>+Ø )

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an evolutionary monte carlo algorithm for predicting dna hybridization

An evolutionary Monte Carlo algorithm for predicting DNA hybridization

JoonShik Kim et al. (2008)

11.05.06.(Fri)

Computational Modeling of Intelligence

JoonShik Kim

neuron and analog computing
Neuron and Analog Computing

Analog Computing

Neuron

spin glass system
Spin glass system

Spin Glass

<S>=

Tanh(J<S>+Ø)

:Mean field theory

dna computing as a spin glass
DNA Computing as a Spin Glass

P∝Exp(-ΣJijSiSj)

Many DNA neighbor

moleculesin 3D

enables the system to

resemble thespin glass.

Microbes in deep sea

slide6

Adaptive steepest

descent

Evolutionary MCMC for DNA

Boltzmann machine

Natural gradient

Hopfield model

Simulated annealing

Deterministic

steepest

descent

Stochastic

annealing

Spin glass

Ising model

slide7

I. Simulating the DNA hybridization

with evolutionary algorithm of

Metropolis and simulated

annealing.

slide8

Introduction

  • We devised a novel evolutionary algorithm
  • applicable to DNA nanoassembly, biochip,
  • and DNA computing.
  • Silicon based results match well the
  • fluorometry and gel electrophoresis
  • biochemistry experiment.
slide9

Theory (1/2)

  • Boltzmann distribution is the one that
  • maximizes the sum of entropies ofboth
  • the system and the environment.
  • Metropolis algorithm drives the system into
  • Boltzmann distribution and simulated
  • annealing drives the system into lowest
  • Gibbs free energy state by slow cooling
  • of the whole system.
slide10

Theory (2/2)

  • We adopted above evolutionary algorithm
  • for simulating the hybridization of DNA
  • molecules.
  • We used only four parameters,
  • ∆HG-C = 9.0 kcal/MBP (mole base pair),
  • ∆HA-T = 7.2 kcal/MBP,
  • ∆Hother= 5.4 kcal/MBP,
  • ∆S = 23 cal/(MBP deg).
  • From (Klump and Ackermann, 1971)
slide11

Algorithm

  • 1. Randomly choose i-th and j-th
  • ssDNA (single stranded DNA).
  • 2. Randomly try an assembly with Metropolis
  • acceptance min(1, e-∆G/kT).
  • 3. We take into account of the detaching
  • process also with Metropolis acceptance.
  • 4. If whole system is in equilibrium then
  • decrease the temperature and repeat
  • process 1-3.
  • 5. Inspect the number of target dsDNA and
  • the number of bonds.
slide12

Target dsDNA (double stranded DNA)

  • 6 types of ssDNA

Sequence (from 5’ to 3’)

Axiom

ㄱQ V ㄱP V R CGTACGTACGCTGAA CTGCCTTGCGTTGAC TGCGTTCATTGTATG

Q V ㄱT V ㄱS TTCAGCGTACGTACG TCAATTTGCGTCAAT TGGTCGCTACTGCTT

S AAGCAGTAGCGACCA

T ATTGACGCAAATTGA

P GTCAACGCAAGGCAG

ㄱR CATACAATGAACGCA

  • Target dsDNA (The arrows are from 5’ to 3’)
slide13

Simulation Results (1/2)

  • The number of bonds vs. temperature
slide14

Simulation Results (2/2)

  • The number of target dsDNA
  • (double stranded DNA) vs. temperature
slide15

Wet-Lab experiment results (1/2)

  • SYBR Green I fluorescent intensity
  • as the cooling of the system
slide16

Wet-Lab experiment results (2/2)

  • Gel electrophoresis of cooled DNA solution
why theorem proving
Why theorem proving?
  • Resolution refutation
  • p→q  ㄱp v q
  • S Λ T → Q, P Λ Q →R, S, T, P then R?

1. Negate R

2. Make a resolution on every axioms.

3. Target dsDNA is a null and its existence

proves the theorem

resolution refutation
Resolution refutation
  • Resolution tree
  • (ㄱQ V ㄱP V R) Λ Q

ㄱP V R