Academic literature on the topic 'Molecular modeling analysis'

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.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate

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.

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.<br>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.

Thesis (Ph.D.)--Case Western Reserve University, 2009<br>Department of Biomedical Engineering Abstract Title from OhioLINK abstract screen (viewed on 10 April 2009) Available online via the OhioLINK ETD Center

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.<br>Doctor of Philosophy<br>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.

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.

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.

Thesis (Ph.D)--Chemistry and Biochemistry, Georgia Institute of Technology, 2009.<br>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.