Browsing by Author "Ni, Ying"

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    Arabinoglucuronoxylan and Arabinoxylan Adsorption onto Regenerated Cellulose Films
    Ni, Ying (Virginia Tech, 2013-09-03)
    Cellulose and hemicelluloses have attracted increasing interest as renewable biopolymers because of their abundance. Furthermore, the recognition of biomass as a sustainable and renewable source of biofuels has driven research into the assembly and disassembly of polymers within plant cell walls. Cellulose thin films are useful in the study of interactions between cellulose and hemicelluloses, and quartz crystal microbalances with dissipation monitoring (QCM-D), surface plasmon resonance (SPR) and atomic force microscopy (AFM) are widely used to investigate polymer adsorption/desorption at liquid/solid interfaces. In this study, smooth trimethylsilyl cellulose (TMSC) films were spincoated onto gold QCM-D sensors and hydrolyzed into ultrathin cellulose films upon exposure to aqueous HCl vapor. The adsorption of arabinoglucuronoxylan (AGX) and arabinoxylan (AX) onto these cellulose surfaces was studied. The effects of structure, molar mass and ionic strength of the solution were considered. Increasing ionic strength increased AGX and AX adsorption onto cellulose. While AGX showed greater adsorption onto cellulose than AX by QCM-D, the trend was reversed in SPR experiments. The combination of QCM-D and SPR data showed a greater amount of water was trapped within the AX films. Both adsorbed AGX and AX films were subsequently visualized by AFM. Images from AFM showed AGX and AX adsorbed as aggregates from water, while AGX and AX adsorbed from CaCl2 yielded smaller xylan particles with more numerous globular structures on the cellulose surfaces. Images from AFM of xylan films on bare gold surfaces also showed layers of uniform aggregates that were consistent with AX and AGX aggregation in solution.
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    A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis
    Grene, Ruth; Heath, Lenwood S.; Li, Song; Collakova, Eva; Elmarakeby, Haitham A.; Ni, Ying; Aghamirzaie, Delasa (Frontiers, 2016-12-23)
    Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.
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    A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis Using Time Series Gene Expression Data
    Ni, Ying (Virginia Tech, 2016-07-08)
    Gene regulatory networks (GRNs) provide a natural representation of relationships between regulators and target genes. Though inferring GRN is a challenging task, many methods, including unsupervised and supervised approaches, have been developed in the literature. However, most of these methods target non-context-specific GRNs. Because the regulatory relationships consistently reprogram under different tissues or biological processes, non-context-specific GRNs may not fit some specific conditions. In addition, a detailed investigation of the prediction results has remained elusive. In this study, I propose to use a machine learning approach to predict GRNs that occur in developmental stage-specific networks and to show how it improves our understanding of the GRN in seed development. I developed a Beacon GRN inference tool to predict a GRN in seed development in Arabidopsis based on a support vector machine (SVM) local model. Using the time series gene expression levels in seed development and prior known regulatory relationships, I evaluated and predicted the GRN at this specific biological process. The prediction results show that one gene may be controlled by multiple regulators. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. The direct targets were detected when I found a match between the promoter regions of the targets and the regulator's binding sequence. Our prediction provides a novel testable hypotheses of a GRN in seed development in Arabidopsis, and the Beacon GRN inference tool provides a valuable model system for context-specific GRN inference.