Relevant bibliographies by topics / DNA model

Academic literature on the topic 'DNA model'

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Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "DNA model"

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Frank-Kamenetskii, M. D., V. V. Anshelevich, and A. V. Lukashin. "Polyelectrolyte model of DNA." Uspekhi Fizicheskih Nauk 151, no. 4 (1987): 595. http://dx.doi.org/10.3367/ufnr.0151.198704b.0595.

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Handelsman, Jo. "Call for Papers: Unique Model Systems." DNA and Cell Biology 27, no. 6 (June 2008): 287. http://dx.doi.org/10.1089/dna.2008.1504.

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Frank-Kamenetskiĭ, M. D., V. V. Anshelevich, and A. V. Lukashin. "Polyelectrolyte model of DNA." Soviet Physics Uspekhi 30, no. 4 (April 30, 1987): 317–30. http://dx.doi.org/10.1070/pu1987v030n04abeh002833.

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Davies, S. W., and D. A. Seale. "DNA Microarray Stochastic Model." IEEE Transactions on Nanobioscience 4, no. 3 (September 2005): 248–54. http://dx.doi.org/10.1109/tnb.2005.853665.

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Middleton, James. "Handy DNA Nucleotide Model." American Biology Teacher 81, no. 3 (March 1, 2019): 193–96. http://dx.doi.org/10.1525/abt.2019.81.3.193.

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A readily available resource to create a model for the study of DNA is the human hand. Students can recognize how structure and function of nucleotides determine structure and function of the DNA molecule by labeling parts of a gloved hand with the parts of a DNA molecule.
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Alireza, Sepehri, Shoorvazi Somayyeh, and Moradi Marjaneh Aliakbar. "Calculating the Specific Heat of DNA by using Phononic Model." Greener Journal of Biological Sciences 3, no. 5 (July 13, 2013): 187–91. http://dx.doi.org/10.15580/gjbs.2013.5.051613617.

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Kelchner, Scot A. "Phylogenetic models and model selection for noncoding DNA." Plant Systematics and Evolution 282, no. 3-4 (July 30, 2008): 109–26. http://dx.doi.org/10.1007/s00606-008-0071-6.

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XU, Jin, and Yue-Ke FAN. "Classical Ramsey Number DNA Computing Model (Ⅱ): Add-Bit-Sequence DNA computing Model." Chinese Journal of Computers 31, no. 12 (October 16, 2009): 2081–89. http://dx.doi.org/10.3724/sp.j.1016.2008.02081.

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ZHAO, YUQI, and HOWARD B. LIEBERMAN. "Schizosaccharomyces pombe:A Model for Molecular Studies of Eukaryotic Genes." DNA and Cell Biology 14, no. 5 (May 1995): 359–71. http://dx.doi.org/10.1089/dna.1995.14.359.

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Mohamad, Abdul Adheem, and Tsukasa Yashiro. "A TOPOLOGICAL MODEL OF DNA REPLICATION WITH DNA-LINKS." Far East Journal of Mathematical Sciences (FJMS) 107, no. 1 (September 27, 2018): 241–55. http://dx.doi.org/10.17654/ms107010241.

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Dissertations / Theses on the topic "DNA model"

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高銘謙 and Ming-him Ko. "A multi-agent model for DNA analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31222778.

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Ko, Ming-him. "A multi-agent model for DNA analysis /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21949116.

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Luchetti, Andrea <1976&gt. "Evolution of repetitive DNA in model arthropods." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2007. http://amsdottorato.unibo.it/338/.

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Friedrich, Tomáš. "Komprese DNA sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237222.

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The increasing volume of biological data requires finding new ways to save these data in genetic banks. The target of this work is design and implementation of a novel algorithm for compression of DNA sequences. The algorithm is based on aligning DNA sequences agains a reference sequence and storing only diferencies between sequence and reference model. The work contains basic prerequisities from molecular biology which are needed for understanding of algorithm details. Next aligment algorithms and common compress schemes suitable for storing of diferencies agains reference sequence are described. The work continues with a description of implementation, which is follewed by derivation of time and space complexity and comparison with common compression algorithms. Further continuation of this thesis is discussed in conclusion.
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Santos, Elmer Buluran. "Biologic response to papillomavirus DNA in COPV model." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621132.

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Arredondo, Ryan. "Properties of Graphs Used to Model DNA Recombination." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/4979.

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A model for DNA recombination uses 4-valent rigid vertex graphs, called assembly graphs. An assembly graph, similarly to the projection of knots, can be associated with an unsigned Gauss code, or double occurrence word. We define biologically motivated reductions that act on double occurrence words and, in turn, on their associated assembly graphs. For every double occurrence word w there is a sequence of reduction operations that may be applied to w so that what remains is the empty word, [epsilon]. Then the nesting index of a word w, denoted by NI(w), is defined to to be the least number of reduction operations necessary to reduce w to [epsilon]. The nesting index is the first property of assembly graphs that we study. We use chord diagrams as tools in our study of the nesting index. We observe two double occurrence words that correspond to the same circle graph, but that have arbitrarily large differences in nesting index values. In 2012, Buck et al. considered the cellular embeddings of assembly graphs into orientable surfaces. The genus range of an assembly graph [Gamma], denoted gr([Gamma]), was defined to be the set of integers g where g is the genus of an orientable surface F into which [Gamma] cellularly embeds. The genus range is the second property of assembly graphs that we study. We generalize the notion of the genus range to that of the genus spectrum, where for each g [isin] gr([Gamma]) we consider the number of orientable surfaces F obtained from [Gamma] by a special construction, called a ribbon graph construction, that have genus g. By considering this more general notion we gain a better understanding of the genus range property. Lastly, we show how one can obtain the genus spectrum of a double occurrence word from the genus spectrums of its irreducible parts, i.e., its double occurrence subwords. In the final chapter we consider constructions of double occurrence words that recognize certain values for nesting index and genus range. In general, we find that for arbitrary values of nesting index [ge] 2 and genus range, there is a double occurrence word that recognizes those values.
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Darko, Janice. "Fluorescent Labeling of Antibiotic Resistant Bacteria Model DNA." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7600.

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Global threats to treatment of bacterial infections due to antibiotic resistance (AR) have been on the rise in recent years. Current diagnostic tests identify bacteria by using blood culture, which takes more than 24 hours. This study focuses on the fluorescent labeling of DNA derived from bacterial AR genes (KPC & VIM) and other model DNAs using oligreen dye (OG) and molecular beacons (MB). A NanoDrop 3300 fluorospectrometer was used to take fluorescence measurements. Linear dynamic range and labeling efficiency were dependent on the following optimized conditions: dilution factor of OG (200 fold), buffer (20 mM Tris HCl, pH 8), and heat treatment of 95 °C for 15 min.Fluorescence analysis of a target DNA with a designed MB showed signal-to-background of 10 with our buffer only and 20 with our buffer and 25% ethanol. I also demonstrated a simple microfluidic device capable of detecting AR genes using model DNAs, magnetic beads, and designed MBs for assays of µ50 L volume. This study provides a first step towards detecting MB-DNA complexes by a simple, low cost, and fast non-amplified method, which may be used to detect AR genes in clinical samples in the future.
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Allegrini, Paolo. "Model for Long-range Correlations in DNA Sequences." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc279189/.

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We address the problem of the DNA sequences developing a "dynamical" method based on the assumption that the statistical properties of DNA paths are determined by the joint action of two processes, one deterministic, with long-range correlations, and the other random and delta correlated. The generator of the deterministic evolution is a nonlinear map, belonging to a class of maps recently tailored to mimic the processes of weak chaos responsible for the birth of anomalous diffusion. It is assumed that the deterministic process corresponds to unknown biological rules which determine the DNA path, whereas the noise mimics the influence of an infinite-dimensional environment on the biological process under study. We prove that the resulting diffusion process, if the effect of the random process is neglected, is an a-stable Levy process with 1 < a < 2. We also show that, if the diffusion process is determined by the joint action of the deterministic and the random process, the correlation effects of the "deterministic dynamics" are cancelled on the short-range scale, but show up in the long-range one. We denote our prescription to generate statistical sequences as the Copying Mistake Map (CMM). We carry out our analysis of several DNA sequences, and of their CMM realizations, with a variety of techniques, and we especially focus on a method of regression to equilibrium, which we call the Onsager Analysis. With these techniques we establish the statistical equivalence of the real DNA sequences with their CMM realizations. We show that long-range correlations are present in exons as well as in introns, but are difficult to detect, since the exon "dynamics" is shown to be determined by theentaglement of three distinct and independent CMM's. Finally we study the validity of the stationary assumption in DNA sequences and we discuss a biological model for the short-range random process based on a folding mechanism of the nucleic acid in the cell nucleus.
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Tello, Cajiao John James 1990. "The influence of the DNA conformation on the radiation-induced DNA damage probabilities = A influência da conformação do DNA nas probabilidades de dano induzido por radiações." [s.n.], 2016. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305738.

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Orientador: Mario Antonio Bernal Rodriguez
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin
Made available in DSpace on 2018-08-30T22:42:12Z (GMT). No. of bitstreams: 1 TelloCajiao_JohnJames_M.pdf: 2614936 bytes, checksum: e5bdfc91b42434b003cad0b5fa850afb (MD5) Previous issue date: 2016
Resumo: O objetivo deste trabalho é estudar a influência da conformação do DNA na probabilidade de dano direto produzido por partículas ionizantes. Além disso, os fundamentos mecanicísticos do modelo Linear-Quadrático são investigadas através de um modelo biofísico desenvolvido neste trabalho, baseado na TADR (Teoria da Ação Dual da Radiação). Para este fim, três modelos geométricos do material genético foram construídos. Os modelos têm resolução atomística e levam em conta ? 10^9 pares de base (bps) nas configurações A,B e Z do DNA. A partir de um único bp, os diferentes níveis organizacionais no interior do núcleo da célula foram criados por meio de transformações lineares. Em seguida, o código Monte Carlo (MC) GEANT4-DNA foi usado para simular o transporte de prótons de 0.5, 1, 5, 7 e 10 MeV assim como de partículas alfa de 2, 5, 7 e 10M eV . O número de partículas em cada caso é de tal modo que as doses absorvidas estão entre 0.5 ? 16Gy. Os três modelos foram consistentes com as dimensões das estruturas reais. Em particular, os modelos foram compatíveis com a exigência de que o diâmetro da cromatina seja de 30 nm, bem como com os volumes bp reportados em outros trabalhos. Os rendimentos tanto das quebras totais quanto das quebras duplas (TSBY e DSBY) foram obtidos para cada qualidade de radiação. Além disso, a probabilidade de impacto (SHP) definida como a razão entre o volume do DNA e o volume núcleo, foi calculada teoricamente e a partir das simulações. O modelo biofísico em conjunto com as simulações MC forneceu o número de lesões letais (N_LL) em função da dose, para prótons de 0,5 e 10 MeV, e para partículas alfa de 2 e 10 MeV . Os N_LL puderam ser divididos em aqueles criados por uma única trajetória e aqueles originados pela interacção de duas trajetórias. Concluiu-se que o TSBY é praticamente determinada pela SHP e depende fracamente da qualidade de radiação incidente. No entanto, o DSBY mostrou forte dependência tanto da conformação do DNA quanto da qualidade de radiação. Isto é devido à relação entre a capacidade de agrupamento das deposições de energia para uma radiação dada e o empacotamento do DNA. Por outro lado, a análise dos mecanismos de produção de dano, baseada na TADR e testada com o modelo biofísico desenvolvido, mostraram que os efeitos de uma única trajetória (de primeira ordem) dependem linearmente com a dose. Além disso, os efeitos inter-trajetórias seguem um comportamento quadrático com a dose, com um termo linear que influencia o mecanismo de primeira ordem. Isto significa que o comportamento linear-quadrático do N_LL com a dose, tem fundamentos mecanicistas, pelo menos, na primeira fase do dano
Abstract: The aim of this work is to study the influence of the DNA conformation on the probability of direct damage induction by ionizing particles. Also, the mechanistic grounds of the Linear-Quadratic radiobiological model are investigated through the eyes of a home-made biophysical model based on the DRAT (Dual Radiation Action Theory). To this end, three geometrical models of the genetic material were constructed. The models have atomistic resolution and account for ? 10^9 base pairs (bps) in the A-, B- and Z-DNA configurations. Starting from a single bp, the different organizational levels inside the cell nucleus were created by means of linear transformations. Next, the Monte Carlo (MC) code GEANT4-DNA was used to simulate the transport of protons of 0.5, 1, 5, 7 and 10 MeV , and alpha particles of 2, 5, 7 and 10 MeV. The number of particles in each case is such that the absorbed doses range between 0.5 Gy and 16 Gy. The three models proved to be consistent with the dimensions of the real structures. In particular, the models were compatible with the 30 nm chromatin fiber diameter requirement as well as with the bp volumes reported in other works. The Total and Double Strand Break Yields (TSBY and DSBY) were obtained for every radiation quality. Also, the Site-Hit Probability (SHP) defined as the total target to the nucleus volume ratio, was computed theoretically and from the simulations. The biophysical model in conjunction with the MC simulations furnished the number of lethal lesions (N_LL) as a function of dose, for protons of 0.5 and 10 MeV , and for alpha particles of 2 and 10 MeV . The N_LL could be split into those created by a single track and those originated by interaction of two tracks. It is concluded that the TSBY is practically determined by the SHP and depends weakly on the incident radiation quality. Nevertheless, the DSBY showed strong dependence on both the DNA conformation and the radiation quality. This is due to the interplay between the energy deposition clustering capacity of a given radiation and the DNA spatial packing. On the other hand, the analysis of the mechanisms of damage production based on the DRAT and tested with the biophysical model developed, showed that single-track (first order) effects depend linearly on the dose. Moreover, inter-track effects follows a quadratic behavior with the dose, having a linear term that influences the first order mechanism. This means that the Linear-Quadratic behavior of the N_LL with the dose, has mechanistic groundings at least at the first stage of the damage
Mestrado
Física
Mestre em Física
1370449/2014
CAPES
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Lee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.

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Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.
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Books on the topic "DNA model"

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Sakalosky, G. P. The predictor model. Gatlinburg, TN: Grams Communications Publication, 1992.

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Rintala, Anne C. DNA repair in a radioresistant breast cancer model system. Sudbury, Ont: Laurentian University, 2000.

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Snapka, Robert M. The SV40 replicon model for analysis of anticancer drugs. Austin, TX: R.G. Landes, 1996.

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The SV40 replicon model for analysis of anticancer drugs. [San Diego, Calif.]: Academic Press, 1996.

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The resonant recognition model of macromolecular bioactivity: Theory and applications. Basel: Birkhäuser Verlag, 1997.

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Sŏ, Yŏng-nok. Yujŏn toksŏng chipʻyo yujŏnja rŭl iyong han yujŏn toksŏng pʻyŏngka model surip yŏnʼgu =: Study on the establishment of evaluation system for genotoxicity using genotoxic biomarker genes. [Seoul]: Sikpʻum Ŭiyakpʻum Anjŏnchʻŏng, 2007.

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Gurgi, Mohamedkamal Ahmed. ESI-MS[n] of anticancer pt[iv] organoamido complexes and their interactions with DNA-model compounds. St. Catharines, Ont: Brock University, Department of Chemistry, 2001.

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Dimitriadi, Konstantina. The use of three pronuclei embryos as a model to analyze the uptake of paternal mitochondrial DNA. Birmingham: University of Birmingham, 1999.

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1959-, Kivshar Y. S., ed. The Frenkel-Kontorova model: Concepts, methods, and applications. Berlin: Springer, 2003.

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Zoya, Ignatova, Martinez-Perez Israel Marck, and SpringerLink (Online service), eds. DNA Computing Models. Boston, MA: Springer-Verlag US, 2008.

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Book chapters on the topic "DNA model"

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Nelson, David O., and Terence P. Speed. "Recovering DNA Sequences from Electrophoresis Data." In Image Models (and their Speech Model Cousins), 141–52. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4056-3_8.

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Ouldridge, Thomas E. "A Novel DNA Model." In Coarse-Grained Modelling of DNA and DNA Self-Assembly, 21–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30517-7_2.

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Pérez-Jiménez, Mario J., and Fernando Sancho-Caparrini. "Solving Knapsack Problems in a Sticker Based Model." In DNA Computing, 161–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48017-x_15.

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Khodor, Julia, and David K. Gifford. "Programmed Mutagenesis Is a Universal Model of Computation." In DNA Computing, 300–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48017-x_28.

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Shiozaki, Masashi, Hirotaka Ono, Kunihiko Sadakane, and Masafumi Yamashita. "A Probabilistic Model of the DNA Conformational Change." In DNA Computing, 274–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11925903_21.

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Sahu, Sudheer, Peng Yin, and John H. Reif. "A Self-assembly Model of Time-Dependent Glue Strength." In DNA Computing, 290–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753681_23.

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Ouldridge, Thomas E. "Thermodynamic Properties of Model DNA." In Coarse-Grained Modelling of DNA and DNA Self-Assembly, 71–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30517-7_6.

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Harvey, Stephen C., and Robert K. Z. Tan. "Development of a Model for DNA Supercoiling." In Unusual DNA Structures, 91–101. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3800-3_6.

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Baskonus, Haci Mehmet, and Carlo Cattani. "Nonlinear Dynamical Model for DNA." In Trends in Mathematics, 115–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3013-1_7.

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Sakakibara, Yasubumi, and Hiroshi Imai. "A DNA-based Computational Model Using a Specific Type of Restriction Enzyme." In DNA Computing, 315–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36440-4_28.

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Conference papers on the topic "DNA model"

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Nishioka, Yuki, Kentaro Doi, and Satoyuki Kawano. "Development of an Electron Scattering Model to Detect Differences in DNA Base Molecules." In ASME-JSME-KSME 2011 Joint Fluids Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajk2011-36031.

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In recent, novel technologies which apply bio-macromolecules to bio-nanodevices attract much attention. Particularly, DNAs have several desirable characteristics: complementary base pairs, self assembly, and electric conductivity. It is expected that high-speed DNA sequencers can be developed by using these specific characteristics of DNAs. In the present study, we develop a theoretical model to analyze the difference of DNA base molecules, in which electron scattering is simulated based on classical electrodynamics and scattering angles are evaluated. Consequently, it is found that scattering angles of the scattered electrons are clearly different from each other.
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Erlander, Stig R. "DNA STRUCTURE: EXPERIMENTAL EVIDENCE AGAINST THE WATSON-CRICK DNA MODEL AND FOR THE ERLANDER DNA MODEL." In XXIst International Carbohydrate Symposium 2002. TheScientificWorld Ltd, 2002. http://dx.doi.org/10.1100/tsw.2002.719.

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Boyda, Denis Leonidovich. "Mathematical model of DNA lesions." In XXI International Baldin Seminar on High Energy Physics Problems. Trieste, Italy: Sissa Medialab, 2013. http://dx.doi.org/10.22323/1.173.0042.

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Sajfert, V., Lj Mašković, and D. Popov. "Model explanation of DNA Transcription." In SIXTH INTERNATIONAL CONFERENCE OF THE BALKAN PHYSICAL UNION. AIP, 2007. http://dx.doi.org/10.1063/1.2733564.

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Lillian, Todd D., N. C. Perkins, and S. Goyal. "Computational Elastic Rod Model Applied to DNA Looping." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34956.

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DNA is a long flexible biopolymer containing genetic information. Proteins often take advantage of DNA’s inherent flexibility to perform their cellular functions. Here we present selected results from our computational studies of the mechanical looping of DNA by the Lactose repressor protein. The Lactose repressor resides in the bacterium E. coli and deforms DNA into a loop as a means of controlling the production of enzymes necessary for digesting lactose. We examine this looping process using a computational rod model [1–3] to understand the strain energy and geometry for the resultant DNA loops. Our model captures the multiple looped conformations of the molecule arising from both multiple boundary conditions and geometric nonlinearities. In addition, the model captures the periodic variation of strain energy with base-pair length as suggested by repression experiments (see, for example, [4, 5]).
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Hirsh, Andrew D., Todd D. Lillian, and N. C. Perkins. "A Model for Highly Strained DNA in a Cavity." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48711.

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A single DNA molecule is a long and flexible biopolymer that contains the genetic code. Building upon the discovery of the iconic double helix over 50 years ago, subsequent studies have emphasized how its biological function is related to the mechanical properties of the molecule. A remarkable system which high-lights the role of DNA bending and twisting is the packing and ejection of DNA into and from viral capsids. A recent 3D reconstruction of bacteriophage φ29 reveals a novel toroidal structure thought to be 30–40 bp of highly bent/twisted DNA contained in a small cavity below the capsid. Here, we extend an elastic rod model for DNA to enable simulation of the toroid as it is compacted and subsequently ejected from a small volume. We compute biologically-realistic forces required to form the toroid and predict ejection times of several nanoseconds.
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Martin, Dan, Mohamad Eid, and Abdulmotaleb El Saddik. "A Haptic Enabled DNA Model Sensing." In 2008 9th International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN '08). IEEE, 2008. http://dx.doi.org/10.1109/i-span.2008.47.

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Liu, Wei, Shouxia Sun, and Ying Guo. "A DNA Computing Model of Perceptron." In 2009 Pacific-Asia Conference on Circuits, Communications and Systems (PACCS). IEEE, 2009. http://dx.doi.org/10.1109/paccs.2009.182.

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Liu, Yaling, and Samir M. Iqbal. "A Mesoscale Model for Molecular Interaction in Functionalized Nanopores." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68542.

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Nanopores have been used to detect DNA translocation and gene detection. However, the interaction between DNA and nanopore is still not well understood due to the small size of DNA/nanopore and dynamic translocation process. Very recently, various chemical modifications have been applied on nanopore surface for improved signal yield and selective detection. Thus, it is important to characterize the interaction between DNA and chemically modified nanopores. This paper intends to develop an understanding of the interaction between DNA and chemically modified nanopore surface and the translocation process of DNA by probing the DNA-nanopore interaction mechanisms through computational modeling. The DNA-nanopore interaction will be explored through a model that links atomistic DNA-nanopore interaction to meso-scale particle dynamics. Critical interrelationships between physical properties of the nanopore (surface properties, sizes, roughness etc.), electric field strength, and translocation kinetics will be established. This research not only advances the molecular-level understanding of the DNA-nanopore interface, but would also help design lab-on-chip devices for molecule based diagnosis.
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Li, Wentian. "DNA segmentation as a model selection process." In the fifth annual international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/369133.369202.

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Reports on the topic "DNA model"

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Jackson, Peter K. DNA Replication Initiator Proteins and Genetic Instability: Creating a Mouse Model for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada392190.

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Mincheff, Milcho S. Naked DNA Immunization for Prevention of Prostate Cancer in a Dunning Rat Prostate Tumor Model. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada417656.

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Kinney, Shannon R. Examination of the Role of DNA Methylation Changes in Prostate Cancer using the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) Model. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada502739.

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Morey Kinney, Shannon R. Examination of the Role of DNA Methylation Changes in Prostate Cancer using the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) Model. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada525616.

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Morey, Shannon R. Examination of the Role of DNA Methylation Changes in Prostate Cancer Using the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) Model. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada483443.

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Christman, Judith K. Role of DNA Methylation in Altering Gene Expression During the Early Stages of Human Breast Cancer Progression in the MCF10AT Xenograft Model. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada418564.

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Christman, Judith K. Role of DNA Methylation in Altering Gene Expression During the Early Stages of Human Breast Cancer Progression in the MCF10AT Xenograft Model. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada426221.

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Macedo, Luciana, and Linda Malkas. The Human Breast Cancer DNA Synthesome Can Serve as a Novel In Vitro Model System for Studying the Mechanism of Action of Anticancer Drugs. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada393926.

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Cucinotta, Francis A. Systems Biology Model of Interactions between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFβ and ATM Signaling. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1335567.

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O'Neill, Peter, and Jennifer Anderson. Systems Biology Model of Interactions Between Tissue Growth Factors and DNA Damage Pathways: Low Dose Response and Cross-Talk in TGFbeta and ATM Signaling. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1158919.

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