an evolutionary monte carlo algorithm for predicting dna hybridization
DESCRIPTION
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+Ø ) - PowerPoint PPT PresentationTRANSCRIPT
1
An evolutionary Monte Carlo algorithm for predicting DNA hybridization
Joon Shik Kim et al. (2008)11.05.06.(Fri)
Computational Modeling of IntelligenceJoon Shik Kim
2
Neuron and Analog Computing
Neuron Analog Computing
3
Spin glass system
Spin Glass
<S>=Tanh(J<S>+Ø):Mean field theory
Hopfield Model
5
DNA Computing as a Spin Glass
Microbes in deep sea
P Exp∝ (-ΣJijSiSj)
Many DNA neighbormolecules in 3Denables the system toresemble the spin glass.
6
Ising model
Spin glass
Stochasticannealing
Deterministicsteepestdescent
Simulated annealing
Boltzmann machine
Evolutionary MCMC for DNA
Hopfield model
Natural gradient
Adaptive steepestdescent
7
I. Simulating the DNA hybridization with evolutionary algorithm of Metropolis and simulated annealing.
8
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.
9
Theory (1/2) • Boltzmann distribution is the one that maximizes the sum of entropies of both 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.
10
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)
11
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.
12
Target dsDNA (double stranded DNA)
ㄱ Q V ㄱ P V R CGTACGTACGCTGAA CTGCCTTGCGTTGAC TGCGTTCATTGTATG Q V ㄱ T V ㄱ S TTCAGCGTACGTACG TCAATTTGCGTCAAT TGGTCGCTACTGCTT S AAGCAGTAGCGACCA T ATTGACGCAAATTGA P GTCAACGCAAGGCAG ㄱ R CATACAATGAACGCA
Axiom Sequence (from 5’ to 3’)• 6 types of ssDNA
• Target dsDNA (The arrows are from 5’ to 3’)
13
Simulation Results (1/2)
• The number of bonds vs. temperature
14
Simulation Results (2/2)
• The number of target dsDNA (double stranded DNA) vs. temperature
15
Wet-Lab experiment results (1/2)
• SYBR Green I fluorescent intensity as the cooling of the system
16
Wet-Lab experiment results (2/2)
• Gel electrophoresis of cooled DNA solution
17
Why theorem proving?
Resolution refutation
p→q ㄱ p v q
S Λ T → Q, P Λ Q →R, S, T, P then R?1. Negate R2. Make a resolution on every axioms.3. Target dsDNA is a null and its existence proves the theorem
18
Resolution refutationResolution tree
( ㄱ Q V ㄱ P V R) Λ Q ㄱ P V R