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A New Methodology for Evaluating the Structural Similarity between Different Phases Using a Dimensionality Reduction Technique.

ABSTRACT: A new methodology for definitively evaluating the structural similarity between different phases in an impartial manner is proposed. This methodology utilizes a dimensionality reduction (DR) technique that was developed in the fields of machine learning and statistics. The basis of the proposed methodology is that the structural similarity between different phases can be evaluated by the geometrical similarity of pair and/or angular distribution functions that reflect the atomic-scale structure of each phase. The DR technique is used for the analysis of this geometrical similarity. In this study, the proposed methodology is applied to evaluate the similarity in the atomic-scale structure, as obtained from molecular dynamics simulations, between amorphous CaCO₃ and CaCO₃ crystal phases in the presence or absence of additives, namely, Mg²⁺ ions, Sr²⁺ ions, and water molecules. The results indicate that in the absence of additives, the structure of the amorphous phase is closer to that of vaterite than to those of calcite or aragonite. However, the degree of structural similarity between the amorphous phase and vaterite decreases if Mg²⁺ ions are present. This tendency is also evident when Sr²⁺ ions are present, although these ions do not influence the structure of the amorphous phase as strongly as Mg²⁺ ions. In addition, the results indicate that at a high water concentration, the amorphous phase is separated into small particles by hydrogen-bonded networks of water molecules and the structure of the amorphous phase more closely approaches that of vaterite. The proposed methodology is widely applicable to the evaluation of the structural similarity between different phases for complex multicomponent systems.

SUBMITTER: Nada H

PROVIDER: S-EPMC6642035 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A New Methodology for Evaluating the Structural Similarity between Different Phases Using a Dimensionality Reduction Technique.

Nada Hiroki H

ACS omega 20180530 5

A new methodology for definitively evaluating the structural similarity between different phases in an impartial manner is proposed. This methodology utilizes a dimensionality reduction (DR) technique that was developed in the fields of machine learning and statistics. The basis of the proposed methodology is that the structural similarity between different phases can be evaluated by the geometrical similarity of pair and/or angular distribution functions that reflect the atomic-scale structure ...[more]

PMID: 31458779

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Project description:BackgroundSystems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.ResultsHere we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares - Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction accuracy was highest when early activation time points in the apoptosis signaling network were used to predict apoptosis rates at later time points. Extended Isomap also outperformed PCA on the AfCS double ligand screen data. Isomap identified more functionally coherent clusters than PCA and captured more information in the first two-components. The Isomap projection performs slightly worse when more signaling networks are analyzed; suggesting that the mapping function between cues and responses becomes increasingly non-linear when large signaling pathways are considered.ConclusionWe developed and applied extended Isomap approach for the analysis of cell signaling networks. Potential biological applications of this method include characterization, visualization and clustering of different treatment conditions (i.e. low and high doses of TNF) in terms of changes in intracellular signaling they induce.

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A New Methodology for Evaluating the Structural Similarity between Different Phases Using a Dimensionality Reduction Technique.

Publications

A New Methodology for Evaluating the Structural Similarity between Different Phases Using a Dimensionality Reduction Technique.

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