An evolutionary Monte Carlo algorithm for predicting DNA hybridization

Kim, Joon Shik; Lee, Jiwoo; Noh, Yung‐Kyun; Park, Jiyoon; Lee, Dong-Yoon; Yang, Kiyull; Chai, Young Gyu; Kim, Jong Chan; Zhang, Byoung-Tak

doi:10.1016/j.biosystems.2007.07.005

Cited by 5 publications

(3 citation statements)

References 24 publications

(33 reference statements)

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“…When |x i and x j | hybridize to make a double strand, the process is considered to be a stochastic process of continuous hybridization and denature with transition probabilities p h and p d . These transition probabilities are governed by the hybridization energy and entropy changes, written as ∆E (kcal) and ∆S (kcal/K) respectively, as well as by the temperature T (K), and we can understand hybridization a Monte Carlo Markov Chain (MCMC) process with the transition probabilities [22],…”

Section: Hybridization Kernel and Positive Definitenessmentioning

confidence: 99%

“…For simplicity of the analysis, we use a previous work showing that the hybridization energy and entropy are simply well-described in terms of three different binding energies. These three binding energies are between A and T , or ∆E AT , between C and G, or ∆E CG , and the binding energy for other pairs, or ∆E Oth [22]. Table 1 shows the experimentally determined values for these binding energies.…”

Section: Hybridization Kernel and Positive Definitenessmentioning

confidence: 99%

“…We also analyze our algorithm using thermodynamics and kinetics for DNA hybridization. We first obtain the thermodynamic properties for DNA hybridization based on the previous work [22,43,45]. DNA thermodynamics explain how the hybridization probability is determined by the change of energy and entropy as well as the temperature.…”

Section: Introductionmentioning

confidence: 99%

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Molecular learning with DNA kernel machines

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We present a computational learning method for bio-molecular classification. This method shows how to design biochemical operations both for learning and pattern classification. As opposed to prior work, our molecular algorithm learns generic classes considering the realization in vitro via a sequence of molecular biological operations on sets of DNA examples. Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space. We analyze the thermodynamic behavior of these learning algorithms, and show simulations on artificial and real datasets as well as demonstrate preliminary wet experimental results using gel electrophoresis.

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Section: Hybridization Kernel and Positive Definitenessmentioning

confidence: 99%

Section: Hybridization Kernel and Positive Definitenessmentioning

confidence: 99%

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Molecular learning with DNA kernel machines

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Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration

Tandon

Kim

Song

et al. 2019

Sci Rep

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Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating numerical constants in mathematics, and estimation of growth behavior via a non-conventional self-assembly in connection with DNA nanotechnology, open a novel perspective to DNA related to computational physics. Here, a method to calculate the numerical value of π, and way to evaluate possible paths of self-avoiding walk with the aid of Monte Carlo simulation, are addressed. Additionally, experimentally obtained variation of the π as functions of DNA concentration and the total number of trials, and the behaviour of self-avoiding random DNA lattice growth evaluated through number of growth steps, are discussed. From observing experimental calculations of π (πexp) obtained by double crossover DNA lattices and DNA rings, fluctuation of πexp tends to decrease as either DNA concentration or the number of trials increases. Based upon experimental data of self-avoiding random lattices grown by the three-point star DNA motifs, various lattice configurations are examined and analyzed. This new kind of study inculcates a novel perspective for DNA nanostructures related to computational physics and provides clues to solve analytically intractable problems.

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A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers

Lee¹,

Lee²,

Kim³

et al. 2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Abstract-We describe a "molecular" evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.

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An evolutionary Monte Carlo algorithm for predicting DNA hybridization

Cited by 5 publications

References 24 publications

Molecular learning with DNA kernel machines

Molecular learning with DNA kernel machines

Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration

A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers

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