Готові списки джерел за темами / Molecular modeling analysis

Добірка наукової літератури з теми "Molecular modeling analysis"

Автор: Grafiati
Опубліковано: 18 травня 2024

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Статті в журналах з теми "Molecular modeling analysis"

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Hanai, Toshihiko. "Molecular Modeling for Quantitative Analysis of Molecular Interaction†." Letters in Drug Design & Discovery 2, no. 3 (May 1, 2005): 232–38. http://dx.doi.org/10.2174/1570180053765192.

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2

Kumawat, Renu, Vineet Sahula, and Manoj S. Gaur. "Probabilistic modeling and analysis of molecular memory." ACM Journal on Emerging Technologies in Computing Systems 11, no. 1 (October 6, 2014): 1–16. http://dx.doi.org/10.1145/2629533.

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Gutiérrez, Alberto, Mert Atilhan, and Santiago Aparicio. "Molecular Modeling Analysis of CO2Absorption by Glymes." Journal of Physical Chemistry B 122, no. 6 (February 6, 2018): 1948–57. http://dx.doi.org/10.1021/acs.jpcb.7b10276.

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Boyle, A. "Polymer chain packing analysis using molecular modeling." Journal of Molecular Graphics 12, no. 3 (September 1994): 219–25. http://dx.doi.org/10.1016/0263-7855(94)80091-x.

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Chahibi, Youssef, Ian F. Akyildiz, and Ilangko Balasingham. "Propagation Modeling and Analysis of Molecular Motors in Molecular Communication." IEEE Transactions on NanoBioscience 15, no. 8 (December 2016): 917–27. http://dx.doi.org/10.1109/tnb.2016.2620439.

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Banks, H. T., N. S. Luke, and J. R. Samuels. "Viscoelasticity in polymers: Phenomenological to molecular mathematical modeling." Numerical Methods for Partial Differential Equations 23, no. 4 (2007): 817–31. http://dx.doi.org/10.1002/num.20250.

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Korendyasev, S. P., A. V. Firsova, D. M. Mordasov, and M. M. Mordasov. "Modeling and Fractal Analysis of Molecular Film Structures." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 23, no. 3 (2017): 527–34. http://dx.doi.org/10.17277/vestnik.2017.03.pp.527-534.

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Obiso, Jr., Richard J., David R. Bevan, and Tracy D. Wilkins. "Molecular Modeling and Analysis of Fragilysin, theBacteroides fragilisToxin." Clinical Infectious Diseases 25, s2 (September 1997): S153—S155. http://dx.doi.org/10.1086/516240.

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Ferreira-Júnior, José Ribamar, Lucas Bleicher, and Mario H. Barros. "Her2p molecular modeling, mutant analysis and intramitochondrial localization." Fungal Genetics and Biology 60 (November 2013): 133–39. http://dx.doi.org/10.1016/j.fgb.2013.06.006.

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Boonyapranai, Kongsak, Hsien-Yu Tsai, Miles Chih-Ming Chen, Supawadee Sriyam, Supachok Sinchaikul, Suree Phutrakul, and Shui-Tien Chen. "Glycoproteomic analysis and molecular modeling of haptoglobin multimers." ELECTROPHORESIS 32, no. 12 (June 2011): 1422–32. http://dx.doi.org/10.1002/elps.201000464.

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Дисертації з теми "Molecular modeling analysis"

1

Noel, Adam. "Modeling and analysis of diffusive molecular communication systems." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54906.

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Анотація:
Diffusive molecular communication (MC) is a promising strategy for the transfer of information in synthetic networks at the nanoscale. If such devices could communicate, then it would expand their cumulative capacity and potentially enable applications such as cooperative diagnostics in medicine, bottom-up fabrication in manufacturing, and sensitive environmental monitoring. Diffusion-based MC relies on the random motion of information molecules due to collisions with other molecules. This dissertation presents a novel system model for three-dimensional diffusive MC where molecules can also be carried by steady uniform flow or participate in chemical reactions. The expected channel impulse response due to a point source of molecules is derived and its statistics are studied. The mutual information between consecutive observations at the receiver is also derived. A simulation framework that accommodates the details of the system model is introduced. A joint estimation problem is formulated for the underlying system model parameters. The Cramer-Rao lower bound on the variance of estimation error is derived. Maximum likelihood estimation is considered and shown to be better than the Cramer-Rao lower bound when it is biased. Peak-based estimators are proposed for the low-complexity estimation of any single channel parameter. Optimal and suboptimal receiver design is considered for detecting the transmission of ON/OFF keying impulses. Optimal joint detection provides a bound on detector performance. The weighted sum detector is proposed as a suboptimal alternative that is more physically realizable. The performance of a weighted sum detector can become comparable to that of the optimal detector when the environment has a mechanism to reduce intersymbol interference. A model for noise sources that continuously release molecules is studied. The time-varying and asymptotic impact of such sources is derived. The model for asymptotic noise is used to approximate the impact of multiuser interference and also the impact of older bits of intersymbol interference.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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2

Aghaei, Amin. "Symmetry-Adapted Molecular Modeling of Nanostructures and Biomembranes." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/295.

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Tremendous advances in nanoscience during the past decades have drawn a new horizon for the future of science. Many biological and structural elements such as DNA, bio-membranes, nanotubes, nanowires and thin films have been studied carefully in the past decades. In this work we target to speed up the computational methods by incorporating the structural symmetries that nanostructures have. In particular, we use the Objective Structures (OS) framework to speed up molecular dynamics (MD), lattice dynamics (phonon analysis) and multiscale methods. OS framework is a generalization of the standard idea for crystal lattices of assuming periodicity of atomic positions with a large supercell. OS not only considers the translational periodicity of the structure, but also other symmetries such as rotational and screw symmetries. In addition to the computational efficiency afforded by Objective Structures, OS provides us with more flexibility in the shape of the unit cell and the form of the external deformation and loading, comparing to using the translational periodicity. This is because the deformation and loading should be consistent in all cells and not all deformations keep the periodicity of the structures. For instance, bending and twisting cannot be modeled with methods using the structure's periodicity. Using OS framework we then carefully studied carbon nanotubes under non-equilibrium deformations. We also studied the failure mechanism of pristine and twisted nanotubes under tensile loading. We found a range of failure mechanisms, including the formation of Stone-Wales defects, the opening of voids, and the motion of atoms out of the cross-section. We also used the OS framework to make concrete analogies between crystalline phonons and normal modes of vibration in non-crystalline but highly symmetric nanostructures.
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Peacock, Darren. "Parallelized multigrid applied to modeling molecular electronics." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101160.

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Анотація:
This thesis begins with a review on the topic of molecular electronics. The purpose of this review is to motivate the need for good theory to understand and predict molecular electronics behaviour. At present the most promising theoretical formalism for dealing with this problem is a combination of density functional theory and nonequilibrium Green's functions (NEGF-DFT). This formalism is especially attractive because it is an ab-initio technique, meaning that it is completely from first principles and does not require any empirical parameters. An implementation of this formalism has been developed by the research group of Hong Guo and is presented and explained here. A few other implementations which are similar but differ in some ways are also discussed briefly to highlight their various advantages and disadvantages.
One of the difficulties of ab-initio calculations is that they can be extremely costly in terms of the computing time and memory that they require. For this reason, in addition to using appropriate approximations, sophisticated numerical analysis tech niques need to be used. One of the bottlenecks in the NEGF-DFT method is solving the Poisson equation on a large real space grid. For studying systems incorporating a gate voltage it is required to be able to solve this problem with nonperiodic boundary conditions. In order to do this a technique called multigrid is used. This thesis examines the multigrid technique and develops an efficient implementation for the purpose of use in the NEGF-DFT formalism. For large systems, where it is necessary to use especially large real space grids, it is desirable to run simulations on parallel computing clusters to handle the memory requirements and make the code run faster. For this reason a parallel implementation of multigrid is developed and tested for performance. The multigrid tool is incorporated into the NEGF-DFT formalism and tested to ensure that it is properly implemented. A few calculations are made on a benzenedithiol system with gold leads to show the effect of an applied gate voltage.
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4

Fang, Yu-Hua. "Quantification of Pharmacokinetics in Small Animals with Molecular Imaging and Compartment Modeling Analysis." Cleveland, Ohio : Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1238635584.

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Thesis (Ph.D.)--Case Western Reserve University, 2009
Department of Biomedical Engineering Abstract Title from OhioLINK abstract screen (viewed on 10 April 2009) Available online via the OhioLINK ETD Center
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Chen, Lulu. "Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96553.

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Tissue heterogeneity, arising from intermingled cellular or tissue subtypes, significantly obscures the analyses of molecular expression data derived from complex tissues. Existing computational methods performing data deconvolution from mixed subtype signals almost exclusively rely on supervising information, requiring subtype-specific markers, the number of subtypes, or subtype compositions in individual samples. We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes directly from mixture expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue or cell-specific markers, determine the number of constituent sub-types, calculate subtype proportions in individual samples, and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, and proteomics data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a deep and unbiased characterization of tissue remodeling in many biomedical contexts. Purified expression profiles from physical experiments provide both ground truth and a priori information that can be used to validate unsupervised deconvolution results or improve supervision for various deconvolution methods. Detecting tissue or cell-specific expressed markers from purified expression profiles plays a critical role in molecularly characterizing and determining tissue or cell subtypes. Unfortunately, classic differential analysis assumes a convenient test statistic and associated null distribution that is inconsistent with the definition of markers and thus results in a high false positive rate or lower detection power. We describe a statistically-principled marker detection method, One Versus Everyone Subtype Exclusively-expressed Genes (OVESEG) test, that estimates a mixture null distribution model by applying novel permutation schemes. Validated with realistic synthetic data sets on both type 1 error and detection power, OVESEG-test applied to benchmark gene expression data sets detects many known and de novo subtype-specific expressed markers. Subsequent supervised deconvolution results, obtained using markers detected by the OVESEG-test, showed superior performance when compared with popular peer methods. While the current debCAM approach can dissect mixed signals from multiple samples into the 'averaged' expression profiles of subtypes, many subsequent molecular analyses of complex tissues require sample-specific deconvolution where each sample is a mixture of 'individualized' subtype expression profiles. The between-sample variation embedded in sample-specific subtype signals provides critical information for detecting subtype-specific molecular networks and uncovering hidden crosstalk. However, sample-specific deconvolution is an underdetermined and challenging problem because there are more variables than observations. We propose and develop debCAM2.0 to estimate sample-specific subtype signals by nuclear norm regularization, where the hyperparameter value is determined by random entry exclusion based cross-validation scheme. We also derive an efficient optimization approach based on ADMM to enable debCAM2.0 application in large-scale biological data analyses. Experimental results on realistic simulation data sets show that debCAM2.0 can successfully recover subtype-specific correlation networks that is unobtainable otherwise using existing deconvolution methods.
Doctor of Philosophy
Tissue samples are essentially mixtures of tissue or cellular subtypes where the proportions of individual subtypes vary across different tissue samples. Data deconvolution aims to dissect tissue heterogeneity into biologically important subtypes, their proportions, and their marker genes. The physical solution to mitigate tissue heterogeneity is to isolate pure tissue components prior to molecular profiling. However, these experimental methods are time-consuming, expensive and may alter the expression values during isolation. Existing literature primarily focuses on supervised deconvolution methods which require a priori information. This approach has an inherent problem as it relies on the quality and accuracy of the a priori information. In this dissertation, we propose and develop a fully unsupervised deconvolution method - deconvolution by Convex Analysis of Mixtures (debCAM) that can estimate the mixing proportions and 'averaged' expression profiles of individual subtypes present in heterogeneous tissue samples. Furthermore, we also propose and develop debCAM2.0 that can estimate 'individualized' expression profiles of participating subtypes in complex tissue samples. Subtype-specific expressed markers, or marker genes (MGs), serves as critical a priori information for supervised deconvolution. MGs are exclusively and consistently expressed in a particular tissue or cell subtype while detecting such unique MGs involving many subtypes constitutes a challenging task. We propose and develop a statistically-principled method - One Versus Everyone Subtype Exclusively-expressed Genes (OVESEG-test) for robust detection of MGs from purified profiles of many subtypes.
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6

Saraf, Sanjeev R. "Molecular characterization of energetic materials." Texas A&M University, 2003. http://hdl.handle.net/1969.1/331.

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Assessing hazards due to energetic or reactive chemicals is a challenging and complicated task and has received considerable attention from industry and regulatory bodies. Thermal analysis techniques, such as Differential Scanning Calorimeter (DSC), are commonly employed to evaluate reactivity hazards. A simple classification based on energy of reaction (-H), a thermodynamic parameter, and onset temperature (To), a kinetic parameter, is proposed with the aim of recognizing more hazardous compositions. The utility of other DSC parameters in predicting explosive properties is discussed. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. Molecular modeling techniques were employed to gain information at the molecular scale to predict calorimetric data. Molecular descriptors, calculated at density functional level of theory, were correlated with DSC data for mono nitro compounds applying Quantitative Structure Property Relationships (QSPR) and yielded reasonable predictions. Such correlations can be incorporated into a software program for apriori prediction of potential reactivity hazards. Estimations of potential hazards can greatly help to focus attention on more hazardous substances, such as hydroxylamine (HA), which was involved in two major industrial incidents in the past four years. A detailed discussion of HA investigation is presented.
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Robertson, Scott C. "Mechanisms of protein kinase activation determined by molecular modeling and mutational analysis /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9938596.

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Sprague, Robin M. "Molecular modeling of DNA with minor groove binding agents and intercalators." Scholarly Commons, 2000. https://scholarlycommons.pacific.edu/uop_etds/539.

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Анотація:
The molecular modeling of several drugs in complexes with deoxyribonucleic acid (DNA) was undet1aken. Selected bis-lexitropsins, based upon NMR and modeling studies of bis-distamycin A, were modeled with an oligonucleotide d(CGAACA TGTTCG)2 using MidasPlus and AMBER 4.0. Intercalators ethidium, ellipticinc. mitoxantrone, and bisantrene were modeled with an oligonucleotide d(CGCG)~ using SpartanPlus and DOCK 4.0. The binding site was prepared from an x-ray study of this oligonucleotide interacting with ditercalinium, a bis-intercalator. The purpost: of this study was to estimate the conformation and orientation of the molecules in tht:ir rt:spcctive binding sites. The mndding study of the bis-lexitropsins showed good agreement with previous modeling studies on distamycin and would be further enhanced by acquisition and interpretation ofNOESY NMR data. The computer modeling study shows that one of the bis-lexitropsins (pyrrole-pyrrole-imidazole, PPI) forms several hydrogen bonds between subunits, which may make it less effective for binding DNA. The other bis-lexitropsin (pyrrole-imidazole-pyrrole, PIP) also forms some interactions between dimers, but is mainly occupied with binding to the DNA and therefore has a more favorable interaction energy for binding to the chosen sequence. The intercalators were similarly agreeable with previous models. Bisantrene has the most favorable interaction energy. It threads its sidechain through the DNA so that while the planar aromatic ring system stacks between base pairs, there is one sidechain in the major groove and one in the minor groove. These extra interactions between the drug and DNA help the interaction to be more favorable.
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Foster, Michael Scott. "Design, synthesis, kinetic analysis, molecular modeling, and pharmacological evaluation of novel inhibitors of peptide amidation." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/31816.

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Анотація:
Thesis (Ph.D)--Chemistry and Biochemistry, Georgia Institute of Technology, 2009.
Committee Chair: Dr. Sheldon W. May; Committee Member: Dr. James C. Powers; Committee Member: Dr. Nicholas Hud; Committee Member: Dr. Niren Murthy; Committee Member: Dr. Stanley H. Pollock. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Wu, Tzong-Ming. "X-ray analysis and molecular modeling of the structure of aromatic polyimide fibers." Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1062601845.

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Книги з теми "Molecular modeling analysis"

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Lü, Jinhu, and Pei Wang. Modeling and Analysis of Bio-molecular Networks. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9144-0.

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2

Protein-protein complexes: Analysis, modeling and drug design. London: Imperial College Press, 2010.

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3

author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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4

Institute for Computer Applications in Science and Engineering., ed. Ranges of applicability for the continuum-beam model in the constitutive analysis of carbon nanotubes: Nanotubes or nano-beams? Hampton, VA: ICASE, NASA Langley Research Center, 2001.

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5

Andriyanova, Mariya, Aslanli Aslanli, Nataliya Basova, Viktor Bykov, Sergey Varfolomeev, Konstantin Gorbunov, Valentin Gorelenkov, et al. ORGANOPHOSPHORUS NEUROTOXINS. ru: Publishing Center RIOR, 2020. http://dx.doi.org/10.29039/02026-5.

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Анотація:
The collective monograph is devoted to discussing the history of creation, studying the properties, neutralizing and using organophosphorus neurotoxins, which include chemical warfare agents, agricultural crop protection chemical agents (herbicides and insecticides) and medicines. The monograph summarizes the results of current scientific research and new prospects for the development of this field of knowledge in the 21st century, including the use of modern physicochemical methods for experimental study and theoretical analysis of biocatalysis and its mechanisms based on molecular modeling with supercomputer power. The book is intended for specialists who are interested in the current state of research in the field of organophosphorus neurotoxins. The monograph will be useful for students, graduate students, researchers specializing in the field of physical chemistry, physicochemical biology, chemical enzymology, toxicology, biochemistry, molecular biology and genetics, biotechnology, nanotechnology and biomedicine.
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Zhou, Xiaobo. Computational systems bioinformatics: Methods and biomedical applications. New Jersey: World Scientific, 2008.

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Wang, Pei, and Jinhu Lü. Modeling and Analysis of Bio-Molecular Networks. Springer Singapore Pte. Limited, 2020.

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Wang, Pei, and Jinhu Lü. Modeling and Analysis of Bio-Molecular Networks. Springer Singapore Pte. Limited, 2021.

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9

Nikoloski, Zoran, and Sergio Grimbs. Network-Based Molecular Biology: Data-Driven Modeling and Analysis. De Gruyter, Inc., 2016.

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Nikoloski, Zoran, and Sergio Grimbs. Network-Based Molecular Biology: Data-Driven Modeling and Analysis. De Gruyter, Inc., 2016.

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Частини книг з теми "Molecular modeling analysis"

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Rose, Beate G., Sandra A. Buckley, Carol Kamps-Holtzapple, Ross C. Beier, and Larry H. Stanker. "Molecular Modeling Studies of Ceftiofur." In Immunoassays for Residue Analysis, 82–98. Washington, DC: American Chemical Society, 1996. http://dx.doi.org/10.1021/bk-1996-0621.ch007.

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2

Veroniki, Areti Angeliki, Georgios Seitidis, Stavros Nikolakopoulos, Marta Ballester, Jessica Beltran, Monique Heijmans, and Dimitris Mavridis. "Modeling in Network Meta-Analysis." In Methods in Molecular Biology, 245–61. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1566-9_15.

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3

Gräter, Frauke. "Strained Molecules: Insights from Force Distribution Analysis." In Modeling of Molecular Properties, 301–10. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527636402.ch19.

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Eriksson, Jan, and David Fenyö. "Modeling Mass Spectrometry-Based Protein Analysis." In Methods in Molecular Biology, 109–17. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-977-2_8.

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Beier, Ross C., Marcel H. Elissalde, and Larry H. Stanker. "Molecular Modeling Studies of the Fumonisin Mycotoxins." In Immunoassays for Residue Analysis, 368–85. Washington, DC: American Chemical Society, 1996. http://dx.doi.org/10.1021/bk-1996-0621.ch029.

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Cui, S. T., and H. D. Cochran. "Molecular Dynamics Modeling of the Molecular Transport Through a Nanpore." In Micro Total Analysis Systems 2001, 203–4. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-1015-3_89.

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Mannhold, Raimund, and Gabriele Cruciani. "Molecular lipophilicity descriptors: a multivariate analysis." In Molecular Modeling and Prediction of Bioactivity, 265–66. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_33.

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8

Lü, Jinhu, and Pei Wang. "Reconstruction of Bio-molecular Networks." In Modeling and Analysis of Bio-molecular Networks, 53–105. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9144-0_2.

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Radchenko, Eugene V., Vladimir A. Palyulin, and Nikolai S. Zefirov. "Molecular Field Topology Analysis (MFTA) as the Basis for Molecular Design." In Molecular Modeling and Prediction of Bioactivity, 460–61. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_118.

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Wang, Ruiqi, Xing-Ming Zhao, and Zengrong Liu. "Modeling and Dynamical Analysis of Molecular Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2139–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02469-6_90.

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Тези доповідей конференцій з теми "Molecular modeling analysis"

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Hill, S. C., M. D. Barnes, W. B. Whitten, and J. M. Ramsey. "Modeling Fluorescence Collection from Single Molecules in Liquid Microspheres." In Laser Applications to Chemical and Environmental Analysis. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/lacea.1996.lwd.7.

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Optimization of molecular detection efficiencies is of central importance in analytical applications involving single molecule detection.1 In addition to limitations imposed on the fraction of molecules which can be detected by the average signal-to-noise ratio, experimental factors such as excitation inhomogeneity and molecular diffusion conspire to further limit "molecular detectability." Recent single molecule detection experiments in microdroplets suggest that such experimental limitations can be significantly reduced primarily because the molecule cannot diffuse away from the excitation volume. However, unlike fluorescence detection from bulk streams where the fluorescence intensity is isotropic in space, the large refractive index change at the surface of microdroplets implies that the fluorescence intensity collected by a lens will be strongly dependent on the position of the molecule within the droplet. In addition, the same refractive index discontinuity at the droplet surface produces a complicated excitation intensity distribution within the droplet as a result of interference between refracted and totally-internally-reflected rays. Thus, issues such as whether molecules near the surface of the sphere can "hide" from the detector as a result of total internal reflection of emission near the droplet surface, or poor excitation efficiency due to the molecule being located in a "shadow" region of the droplet will have a potential effect on molecular detection efficiencies. These questions are nontrivial to address in a quantitative way. Here we discuss development of numerical tools for modeling the fluorescence collected from a single molecule within a microdroplet as a function of position, orientation, and detection geometry based on the semiclassical electrodynamics formalism developed by Chew2 for light scattering in dielectric microspheres. In addition we also examine effects of excitation inhomogeneity within the sphere, molecular diffusion, and transition rate modification in order to obtain a realistic model of molecular detection efficiencies in microdroplets.
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2

SOMOGYI, ROLAND, HIROAKI KITANO, SATORU MNANO, and QIANG ZHENG. "MOLECULAR NETWORK MODELING AND DATA ANALYSIS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814447331_0027.

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3

Sandmann, Werner. "Applicability of Importance Sampling to Coupled Molecular Reactions." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0052.

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4

Rakesh, L., Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Modeling and Bio molecular Self-assembly via Molecular Dynamics and Dissipative Particle Dynamics." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2009: Volume 1 and Volume 2. AIP, 2009. http://dx.doi.org/10.1063/1.3241606.

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5

Zhou, Taimei, Xueying Zheng, Deqing Yi, and Qingying Zhang. "Molecular Modeling and Structure Analysis of S100 Calcium Binding Protein A14: Molecular Modeling and Structure Analysis of S100A14." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5301740.

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6

Shepelev, Igor A., Ivan D. Kolesnikov, and Elena A. Korznikova. "Analysis of a crowdion propagated in an extremely heated tungsten." In Laser Physics, Photonic Technologies, and Molecular Modeling, edited by Vladimir L. Derbov. SPIE, 2022. http://dx.doi.org/10.1117/12.2626247.

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7

Doss, Derek J., Jon S. Heiselman, Ma Luo, Logan W. Clements, Michael I. Miga, Daniel Brown, and Filip Banovac. "Quantitative imaging analysis to guide biopsy for molecular biomarkers." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Baowei Fei and Cristian A. Linte. SPIE, 2019. http://dx.doi.org/10.1117/12.2513588.

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8

Mezey, Paul G. "Molecular modeling: An open invitation for applied mathematics." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825416.

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9

Kulkova, E. Yu, M. G. Khrenova, I. V. Polyakov, and A. V. Nemukhin. "Computer modeling of properties of complex molecular systems." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912965.

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10

Lavrova, Anastasia I., Eugene Postnikov, and Diljara Esmedljaeva. "Mathematical modeling of cavity development in lung tuberculosis." In Computations and Data Analysis: from Molecular Processes to Brain Functions, edited by Dmitry E. Postnov. SPIE, 2021. http://dx.doi.org/10.1117/12.2588475.

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Звіти організацій з теми "Molecular modeling analysis"

1

Rafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593390.bard.

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The proposed research was directed at determining the activation/binding domains and gene regulation of the PBAN-R’s thereby providing information for the design and screening of potential PBAN-R-blockers and to indicate possible ways of preventing the process from proceeding to its completion. Our specific aims included: (1) The identification of the PBAN-R binding domain by a combination of: (a) in silico modeling studies for identifying specific amino-acid side chains that are likely to be involved in binding PBAN with the receptor and; (b) bioassays to verify the modeling studies using mutant receptors, cell lines and pheromone glands (at tissue and organism levels) against selected, designed compounds to confirm if compounds are agonists or antagonists. (2) The elucidation ofthemolecular regulationmechanisms of PBAN-R by:(a) age-dependence of gene expression; (b) the effect of hormones and; (c) PBAN-R characterization in male hair-pencil complexes. Background to the topic Insects have several closely related G protein-coupled receptors (GPCRs) belonging to the pyrokinin/PBAN family, one with the ligand pheromone biosynthesis activating neuropeptide or pyrokinin-2 and another with diapause hormone or pyrokinin-1 as a ligand. We were unable to identify the diapause hormone receptor from Helicoverpa zea despite considerable effort. A third, related receptor is activated by a product of the capa gene, periviscerokinins. The pyrokinin/PBAN family of GPCRs and their ligands has been identified in various insects, such as Drosophila, several moth species, mosquitoes, Triboliumcastaneum, Apis mellifera, Nasoniavitripennis, and Acyrthosiphon pisum. Physiological functions of pyrokinin peptides include muscle contraction, whereas PBAN regulates pheromone production in moths plus other functions indicating the pleiotropic nature of these ligands. Based on the alignment of annotated genomic sequences, the primary and secondary structures of the pyrokinin/PBAN family of receptors have similarity with the corresponding structures of the capa or periviscerokinin receptors of insects and the neuromedin U receptors found in vertebrates. Major conclusions, solutions, achievements Evolutionary trace analysisof receptor extracellular domains exhibited several class-specific amino acid residues, which could indicate putative domains for activation of these receptors by ligand recognition and binding. Through site-directed point mutations, the 3rd extracellular domain of PBAN-R was shown to be critical for ligand selection. We identified three receptors that belong to the PBAN family of GPCRs and a partial sequence for the periviscerokinin receptor from the European corn borer, Ostrinianubilalis. Functional expression studies confirmed that only the C-variant of the PBAN-R is active. We identified a non-peptide agonist that will activate the PBAN-receptor from H. zea. We determined that there is transcriptional control of the PBAN-R in two moth species during the development of the pupa to adult, and we demonstrated that this transcriptional regulation is independent of juvenile hormone biosynthesis. This transcriptional control also occurs in male hair-pencil gland complexes of both moth species indicating a regulatory role for PBAN in males. Ultimate confirmation for PBAN's function in the male tissue was revealed through knockdown of the PBAN-R using RNAi-mediated gene-silencing. Implications, both scientific and agricultural The identification of a non-peptide agonist can be exploited in the future for the design of additional compounds that will activate the receptor and to elucidate the binding properties of this receptor. The increase in expression levels of the PBAN-R transcript was delineated to occur at a critical period of 5 hours post-eclosion and its regulation can now be studied. The mysterious role of PBAN in the males was elucidated by using a combination of physiological, biochemical and molecular genetics techniques.
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2

Gurevitz, Michael, William A. Catterall, and Dalia Gordon. face of interaction of anti-insect selective toxins with receptor site-3 on voltage-gated sodium channels as a platform for design of novel selective insecticides. United States Department of Agriculture, December 2013. http://dx.doi.org/10.32747/2013.7699857.bard.

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Voltage-gated sodium channels (Navs) play a pivotal role in excitability and are a prime target of insecticides like pyrethroids. Yet, these insecticides are non-specific due to conservation of Navs in animals, raising risks to the environment and humans. Moreover, insecticide overuse leads to resistance buildup among insect pests, which increases misuse and risks. This sad reality demands novel, more selective, insect killers whose alternative use would avoid or reduce this pressure. As highly selective insect toxins exist in venomous animals, why not exploit this gift of nature and harness them in insect pest control? Many of these peptide toxins target Navs, and since their direct use via transformed crop plants or mediator microorganisms is problematic in public opinion, we focus on the elucidation of their receptor binding sites with the incentive of raising knowledge for design of toxin peptide mimetics. This approach is preferred nowadays by agro-industries in terms of future production expenses and public concern. However, characterization of a non-continuous epitope, that is the channel receptor binding site for such toxins, requires a suitable experimental system. We have established such a system within more than a decade and reached the stage where we employ a number of different insect-selective toxins for the identification of their receptor sites on Navs. Among these toxins we wish to focus on those that bind at receptor site-3 and inhibit Nav inactivation because: (1) We established efficient experimental systems for production and manipulation of site-3 toxins from scorpions and sea anemones. These peptides vary in size and structure but compete for site-3 on insect Navs. Moreover, these toxins exhibit synergism with pyrethroids and with other channel ligands; (2) We determined their bioactive surfaces towards insect and mammalian receptors (see list of publications); (3) We found that despite the similar mode of action on channel inactivation, the preference of the toxins for insect and mammalian channel subtypes varies greatly, which can direct us to structural features in the basis of selectivity; (4) We have identified by channel loop swapping and point mutagenesis extracellular segments of the Navinvolved with receptor site-3. On this basis and using channel scanning mutagenesis, neurotoxin binding, electrophysiological analyses, and structural data we offer: (i) To identify the residues that form receptor site-3 at insect and mammalian Navs; (ii) To identify by comparative analysis differences at site-3 that dictate selectivity toward various Navs; (iii) To exploit the known toxin structures and bioactive surfaces for modeling their docking at the insect and mammalian channel receptors. The results of this study will enable rational design of novel anti-insect peptide mimetics with minimized risks to human health and to the environment. We anticipate that the release of receptor site-3 molecular details would initiate a worldwide effort to design peptide mimetics for that site. This will establish new strategies in insect pest control using alternative insecticides and the combined use of compounds that interact allosterically leading to increased efficiency and reduced risks to humans or resistance buildup among insect pests.
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