6,965 research outputs found

    NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

    Full text link
    Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

    Rigidity and flexibility of biological networks

    Full text link
    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

    A survey of statistical network models

    Full text link
    Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

    A model of large-scale proteome evolution

    Get PDF
    The next step in the understanding of the genome organization, after the determination of complete sequences, involves proteomics. The proteome includes the whole set of protein-protein interactions, and two recent independent studies have shown that its topology displays a number of surprising features shared by other complex networks, both natural and artificial. In order to understand the origins of this topology and its evolutionary implications, we present a simple model of proteome evolution that is able to reproduce many of the observed statistical regularities reported from the analysis of the yeast proteome. Our results suggest that the observed patterns can be explained by a process of gene duplication and diversification that would evolve proteome networks under a selection pressure, favoring robustness against failure of its individual components

    Computational Labeling, Partitioning, and Balancing of Molecular Networks

    Get PDF
    Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes

    Secretory RING finger proteins function as effectors in a grapevine galling insect.

    Get PDF
    BackgroundAll eukaryotes share a conserved network of processes regulated by the proteasome and fundamental to growth, development, or perception of the environment, leading to complex but often predictable responses to stress. As a specialized component of the ubiquitin-proteasome system (UPS), the RING finger domain mediates protein-protein interactions and displays considerable versatility in regulating many physiological processes in plants. Many pathogenic organisms co-opt the UPS through RING-type E3 ligases, but little is known about how insects modify these integral networks to generate novel plant phenotypes.ResultsUsing a combination of transcriptome sequencing and genome annotation of a grapevine galling species, Daktulosphaira vitifoliae, we identified 138 putatively secretory protein RING-type (SPRINGs) E3 ligases that showed structure and evolutionary signatures of genes under rapid evolution. Moreover, the majority of the SPRINGs were more expressed in the feeding stage than the non-feeding egg stage, in contrast to the non-secretory RING genes. Phylogenetic analyses indicated that the SPRINGs formed clusters, likely resulting from species-specific gene duplication and conforming to features of arthropod host-manipulating (effector) genes. To test the hypothesis that these SPRINGs evolved to manipulate cellular processes within the plant host, we examined SPRING interactions with grapevine proteins using the yeast two-hybrid assay. An insect SPRING interacted with two plant proteins, a cellulose synthase, CSLD5, and a ribosomal protein, RPS4B suggesting secretion reprograms host immune signaling, cell division, and stress response in favor of the insect. Plant UPS gene expression during gall development linked numerous processes to novel organogenesis.ConclusionsTaken together, D. vitifoliae SPRINGs represent a novel gene expansion that evolved to interact with Vitis hosts. Thus, a pattern is emerging for gall forming insects to manipulate plant development through UPS targeting
    • …
    corecore