6,965 research outputs found
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
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
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
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
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
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.
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
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