Coarse-Grained Molecular Dynamics Modeling of Interactions between Biomolecules and Nanostructures

In modern biotechnology and medicine realm, understanding interactions between biomolecules and nanostructures at molecular level is essential for designs of nanoscale diagnostic or therapeutic devices. Due to the limited time and length scales a full-atomistic molecular dynamics system can reach, the coarse-grained molecular dynamics technique is continuously sought to describe interactions between biomolecules and nanostructures. Here, the coarse-grained molecular dynamics is applied to different cases for revealing complex interactions between biomolecules and nanostructures. The first case in this dissertation is to quantify the biomarker detection process, solve the puzzle of biosensor detection at ultralow concentration and expedite the technique of early cancer diagnosis. Antibodies have been used as bioreceptors in bio-diagnostic devices for decades, whose performances are affected by various factors such as orientation, density, and local environment. While there are extensive works on designing and fabrication of various biosensors, little is known about the molecular level interactions between antibodies coated on sensor surfaces and biomarkers suspended in medium. Thus, a coarse-grained model for biomarkers binding on an antibody-functionalized biosensor surface is constructed to study effects of surface properties and external parameters on antibody orientation and biomarkers binding time. The surface interaction type is found to significantly influence the antibody orientation and biomarker binding time. A proper electric field range is discovered to not only well-orientate antibodies but also steer biomarkers toward the surface, consequently reducing the binding time of biomarkers by two orders of magnitude. Moreover, a suitable surface coating density of antibodies has been proposed to help antibody orientation as well as biomarker binding. These findings can be used for rational design of biosensors with higher efficiency and more sensitive detections.For the subsequent cases, the coarse-grained molecular dynamics model for the DNA-NP conjugate which is assembled by DNA and nanoparticles is established and used as building blocks for constructing one dimensional nanoworm and two dimensional nanosheet structures. Their mechanical properties are tested and potential applications are discussed with the developed model. The nanoworm structure, which can be applied in fields of drug targeting, image probing and thermal therapies, has been assembled by DNA-nanoparticle conjugates. Subsequently, its mechanical properties have been investigated due to their importance on the structural stability, transport and circulations of the nanoworm. Stiffness and strengths of the nanoworm under different deformation types are studied by coarse-grained molecular dynamics simulations. Effects of temperature, DNA coating density and particle size on mechanical properties of nanoworms are also thoroughly investigated. Results show that both resistance and strength of the nanoworm are the weakest along the axial direction, indicating it is more prone to be ruptured by a stretching force. In addition, DNA strands are found to be more important than nanoparticles in determining mechanical properties of the nanoworm. Moreover, both strength and resistance in regardless of directions are proved to be enhanced by decreasing the temperature, raising the DNA coating density and enlarging the particle size. This study is capable of serving as guidance for designing nanoworms with optimal mechanical strengths for applications.Two dimensional arrays of DNA-nanoparticle conjugates have also been fabricated and become a promising platform for developments of chemical sensor, molecular circuit, and mechanical analysis tools. Whatever it is used for, the mechanical properties affect its efficiency and efficacy in large extent. Thus, its mechanical properties have been scrutinized by the coarse-grained molecular dynamics simulation model. Stress-strain curves of the lattice under shearing and stretching are obtained and analyzed. Different hairpin structures have been used to connect adjacent DNA-nanoparticle conjugates and proven to influence stress-strain relationship of 2D array. Effects of physical conditions such as the temperature and salt concentration on mechanical properties of the 2D lattice are also investigated. Results found that 2D lattice behave like a macroscopic paper or alumina foil, whose force-displacement curve is in great agreement with that of elastic sheet. The 2D nanosheet is quite stable at 293 K with a salt concentration of 100 mM. Based on aforementioned results, a numerical model is proposed for the stress-strain relationship of 2D array. In future, this numerical model will be evaluated by our experimental results.Future work includes the investigation on mechanical response of three dimensional nanocrystal constructed by the same DNA-NP conjugates and a multiscale modeling of red blood cell membrane rupturing process

Supervised cross-modal factor analysis for multiple modal data classification

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods

Prediction of PEV Adoption with Agent-Based Parameterized Bass Network Diffusion Model

Although the growing electric vehicle (EV) population is leading us into a more sustainable world, it is also bringing challenges for the manufacturers's production planning, the charging facility providers's expansion plan, and the energy system's adaption to greater electricity demand. To tackle these challenges, a model to predict EV growth in geographical scope would be helpful. In this study, an agent-based parameterized bass network diffusion model was developed for EV population data in Washington. The model included income levels and number of neighbors adopted as two key factors in determining EV diffusion probabilities. With the parameters estimated from simulation, the resulting model achieve a high estimation accuracy for EV adoption in Washington in both temporal and geographical scopes. This model could be used to predict EV growth in Washington, and to be adopted to other geographical areas

The model of rat lipid metabolism disorder induced by chronic stress accompanying high-fat-diet

<p>Abstract Objective</p> <p>To develop an animal model of Lipid Metabolism Disorder, which conforms to human clinical characteristic. Methods: There were 24 male Wistar rats that were randomly divided into 3 groups with 8 rats in each. They were group A (normal diet), group B (high-fat-diet), group C (chronic stress+ high-fat-diet). Group A was fed with normal diet, while group B and C were fed with high-fat-diet, going on for 55 days. From the 35th day, group B and C received one time of daily chronic stress, going on for 21 days. After that, the activities of the serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and the levels of the serum triglyceride (TG), Cholesterol (Ch), high-density lipoprotein-Cholesterol (HDL-C) and liver TG were evaluated. Results: Compared with group A, the activities of the serum ALT and AST, and the levels of the serum CH, TG, HDL-C and liver TG were found to be markedly increased, when the level of HDL-C was markedly decreased in group B and C, and the results of group C was more obviously. Conclusion: Chronic stress and high-fat-diet have the synergistic action in rat's Lipid Metabolism Disorder. They lead to a model of Lipid Metabolism Disorder, which conforms to human clinical characteristic much better.</p

A Smart pH-Sensitive Delivery System for Enhanced Anticancer Efficacy via Paclitaxel Endosomal Escape

Micelles are highly attractive nano-drug delivery systems for targeted cancer therapy. While they have been demonstrated to significantly alleviate the side-effects of their cargo drugs, the therapy outcomes are usually suboptimal partially due to ineffective drug release and endosome entrapment. Stimulus-responsive nanoparticles have allowed controlled drug release in a smart fashion, and we want to use this concept to design novel micelles. Herein, we reported pH-sensitive paclitaxel (PTX)-loaded poly (ethylene glycol)-phenylhydrazone-dilaurate (PEG-BHyd-dC12) micelles (PEG-BHyd-dC12/PTX). The micelles were spherical, with an average particle size of ∼135 nm and a uniform size distribution. The pH-responsive properties of the micelles were certified by both colloidal stability and drug release profile, where the particle size was strikingly increased accompanied by faster drug release as pH decreased from 7.4 to 5.5. As a result, the micelles exhibited much stronger cytotoxicity than the pH-insensitive counterpart micelles against various types of cancer cells due to the hydrolysis of the building block polymers and subsequent rapid PTX release. Overall, these results demonstrate that the PEG-BHyd-dC12 micelle is a promising drug delivery system for cancer therapy