scholarly journals Hypergraph models of biological networks to identify genes critical to pathogenic viral response

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Song Feng ◽  
Emily Heath ◽  
Brett Jefferson ◽  
Cliff Joslyn ◽  
Henry Kvinge ◽  
...  
Keyword(s):  
Biological Networks ◽  
Viral Infections ◽  
Protein Complexes ◽  
Biological Systems ◽  
Experimental Conditions ◽  
Data Set ◽  
Genomic Expression ◽  
Viral Response ◽  
Complex Relationships ◽  
Critical Components

Abstract Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

scholarly journals Graph Theoretic Techniques for Clustering and Biclustering gene expression data.

2012 ◽  
pp. 173-181
Author(s):  
Prangyaparamita Mohapatra ◽  
Tripti Swarnkar
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Microarray Technology ◽  
Clustering Methods ◽  
Experimental Conditions ◽  
Data Set

DNA microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes during biological processes and across collections of related samples. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the gene expression matrix have been proposed to date. This simultaneous clustering, usually designated by biclustering, seeks to find submatrices that are subgroups of genes and subgroups of columns, where the genes exhibit highly correlated activities for every condition. This type of algorithms has also been proposed and used in other fields, such as information retrieval and data mining. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches.

scholarly journals Analytical Rheology of Honey: A State-of-the-Art Review

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1709
Author(s):  
Célia Faustino ◽  
Lídia Pinheiro
Keyword(s):  
Sugar Content ◽  
Rheological Behaviour ◽  
Principal Component ◽  
Rheological Parameters ◽  
Experimental Conditions ◽  
Thixotropic Behaviour ◽  
Ancient Times ◽  
Medicinal Properties ◽  
Complex Relationships ◽  
Temperature Time

Honey has been used as a nutraceutical product since ancient times due to its nutritional and medicinal properties. Honey rheology influences its organoleptic properties and is relevant for processing and quality control. This review summarizes the rheological behaviour of honeys of different botanical source(s) and geographical locations that has been described in the literature, focusing on the relation between rheological parameters, honey composition (moisture, water activity, sugar content, presence of colloidal matter) and experimental conditions (temperature, time, stress, shear rate). Both liquid and crystallized honeys have been addressed. Firstly, the main mathematical models used to describe honey rheological behaviour are presented highlighting moisture and temperature effects. Then, rheological data from the literature regarding distinct honey types from different countries is analysed and results are compared. Although most honeys are Newtonian fluids, interesting shear-thinning and thixotropic as well as anti-thixotropic behaviour have been described for some types of honey. Rheological parameters have also been successfully applied to identify honey adulteration and to discriminate between different honey types. Several chemometric techniques have also been employed to obtain the complex relationships between honey physicochemical and rheological properties, including partial least squares (PLS), principal component analysis (PCA) and artificial neural networks (ANN).

scholarly journals The model of prediction of the inhibition of neuraminidases of influenza A and B based on a reduced set of energy terms

2019 ◽  
Vol 65 (6) ◽  
pp. 520-525 ◽  
Author(s):  
A.V. Mikurova ◽  
V.S. Skvortsov
Keyword(s):  
Influenza Virus ◽  
Potential Energy ◽  
Correlation Coefficient ◽  
Influenza A ◽  
Protein Complexes ◽  
Computation Time ◽  
Inhibitor Protein ◽  
Data Set ◽  
Influenza Virus A ◽  
Calculation Procedures

The overall model for prediction of IC₅₀ values for inhibitors of neuraminidase influenza virus A and B has been created. It combines data about IC₅₀ values of complexes of 40 variants of neuraminidases of influenza A (7 serotypes) and B and three known inhibitors (oseltamivir, zanamivir, peramivir). The model also uses only data of enthalpy contributions to the potential energy of inhibitor/protein and substrate (MUNANA)/protein complexes. The calculation procedures are ported to use software with support of GPU accelerators, that significant decrease the computation time. The corresponding correlation coefficient (R²) for pIC₅₀ prediction was within 0.45-0.58, the SEM values of around 0.7 (the range of used pIC₅₀ data set is from 4.55 to 10.22).

Modules in Biological Networks

2013 ◽  
pp. 637-663
Author(s):  
Bing Zhang ◽  
Zhiao Shi
Keyword(s):  
Biological Networks ◽  
Protein Complex ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Single Gene ◽  
Biological Systems ◽  
Function Prediction ◽  
Diverse Group ◽  
Biological Studies ◽  
Gene Level

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.

Integrative Data Analysis for Biological Discovery

2011 ◽  
pp. 1058-1065
Author(s):  
Sai Moturu
Keyword(s):  
Molecular Biology ◽  
Biological System ◽  
Molecular Level ◽  
Biological Systems ◽  
Integrated Analysis ◽  
John Muir ◽  
Complex Biological System ◽  
Protein Levels ◽  
Lofty Goal ◽  
Complex Relationships

As John Muir noted, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe” (Muir, 1911). In tune with Muir’s elegantly stated notion, research in molecular biology is progressing toward a systems level approach, with a goal of modeling biological systems at the molecular level. To achieve such a lofty goal, the analysis of multiple datasets is required to form a clearer picture of entire biological systems (Figure 1). Traditional molecular biology studies focus on a specific process in a complex biological system. The availability of high-throughput technologies allows us to sample tens of thousands of features of biological samples at the molecular level. Even so, these are limited to one particular view of a biological system governed by complex relationships and feedback mechanisms on a variety of levels. Integrated analysis of varied biological datasets from the genetic, translational, and protein levels promises more accurate and comprehensive results, which help discover concepts that cannot be found through separate, independent analyses. With this article, we attempt to provide a comprehensive review of the existing body of research in this domain.

Diffusion in Biological Systems

Drug Delivery ◽  
2001 ◽  
Author(s):  
W. Mark Saltzman
Keyword(s):  
Diffusion Coefficient ◽  
Diffusion Coefficients ◽  
Molecular Diffusion ◽  
Biological Systems ◽  
Biological Tissues ◽  
Solute Concentration ◽  
Diffusion Resistance ◽  
Experimental Conditions ◽  
Composition Structure ◽  
Reliable Mechanism

Drug diffusion is an essential mechanism for drug dispersion throughout biological systems. Diffusion is fundamental to the migration of agents in the body and, as we will see in Chapter 9, diffusion can be used as a reliable mechanism for drug delivery. The rate of diffusion (i.e., the diffusion coefficient) depends on the architecture of the diffusing molecule. In the previous chapter a hypothetical solute with a diffusion coefficient of 10-7 cm2/s was used to describe the kinetics of diffusional spread throughout a region. Therapeutic agents have a multitude of sizes and shapes and, hence, diffusion coefficients vary in ways that are not easily predictable. Variability in the properties of agents is not the only difficulty in predicting rates of diffusion. Biological tissues present diverse resistances to molecular diffusion. Resistance to diffusion also depends on architecture: tissue composition, structure, and homogeneity are important variables. This chapter explores the variation in diffusion coefficient for molecules of different size and structure in physiological environments. The first section reviews some of the most important methods used to measure diffusion coefficients, while subsequent sections describe experimental measurements in media of increasing complexity: water, membranes, cells, and tissues. Diffusion coefficients are usually measured by observing changes in solute concentration with time and/or position. In most situations, concentration changes are monitored in laboratory systems of simple geometry; equally simple models (such as the ones developed in Chapter 3) can then be used to determine the diffusion coefficient. However, in biological systems, diffusion almost always occurs in concert with other phenomena that also influence solute concentration, such as bulk motion of fluid or chemical reaction. Therefore, experimental conditions that isolate diffusion—by eliminating or reducing fluid flows, chemical reactions, or metabolism—are often employed. Certain agents are eliminated from a tissue so slowly that the rate of elimination is negligible compared to the rate of dispersion. These molecules can be used as “tracers” to probe mechanisms of dispersion in the tissue, provided that elimination is negligible during the period of measurement. Frequently used tracers include sucrose [1, 2], iodoantipyrene [3], inulin [1], and size-fractionated dextran [3, 4].

scholarly journals Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm

2020 ◽  
Author(s):  
Julian Hofer ◽  
Albert Ansmann ◽  
Dietrich Althausen ◽  
Ronny Engelmann ◽  
Holger Baars ◽  
...  
Keyword(s):  
Optical Properties ◽  
Full Range ◽  
Anthropogenic Pollution ◽  
Data Set ◽  
Central Asian ◽  
Lidar Ratio ◽  
Ground Based Lidar ◽  
Complex Relationships ◽  
532 Nm ◽  
Depolarization Ratios

Abstract. For the first time, a dense data set of particle extinction-to-backscatter ratios (lidar ratios), linear depolarization ratios, and backscatter- and extinction-related Ångström exponents for a Central Asian site are presented. The observations were performed with a continuously running multiwavelength polarization Raman lidar at Dushanbe, Tajikistan, during an 18-month campaign (March 2015 to August 2016). The presented seasonally resolved observations fill an important gap in the data base of aerosol optical properties used in aerosol typing efforts with spaceborne lidars and ground-based lidar networks. Lidar ratios and depolarization ratios are also basic input parameters in spaceborne lidar data analyses and in efforts to harmonize long-term observations with different space lidar systems operated either at 355 or 532 nm. As a general result, the found optical properties reflect the large range of occurring aerosol mixtures consisting of long-range-transported dust (from the Middle East and the Sahara), regional desert, soil, and salt dust, and anthropogenic pollution. The full range from highly polluted to pure dust situations could be observed. Typical dust depolarization ratios of 0.23–0.29 (355 nm) and 0.30–0.35 (532 nm) were observed. In contrast, comparably low lidar ratios were found. Dust lidar ratios at 532 nm accumulated around 35–40 sr and were even lower for regional background dust conditions (20–30 sr). The reason for these low values may be partly related to the direct emission and emission of re-suspended salt dust (initially originated from numerous desiccating lakes and the Aralkum desert). Detailed correlation studies (e.g., lidar ratio vs. depolarization ratios and Ångström exponent vs. lidar ratio and vs. depolarization ratio) are presented to illuminate the complex relationships between the observed optical properties and to identify the contributions of anthropogenic haze, dust, and background aerosol to the overall aerosol mixtures found within the 18-month campaign.

Genomic analysis of neuroendocrine development of fetal brain-pituitary-adrenal axis in late gestation

2006 ◽  
Vol 24 (3) ◽  
pp. 218-224 ◽  
Author(s):  
Maureen Keller-Wood ◽  
Melanie J. Powers ◽  
Jason A. Gersting ◽  
Nyima Ali ◽  
Charles E. Wood
Keyword(s):  
Brain Stem ◽  
Hpa Axis ◽  
Fetal Brain ◽  
Genomic Analysis ◽  
Brain Regions ◽  
Late Gestation ◽  
Fetal Sheep ◽  
Genomic Expression ◽  
Feedback Sensitivity ◽  
Critical Components

The present study was performed to identify the changes in genomic expression of critical components of the hypothalamus-pituitary-adrenal (HPA) axis in the second half of gestation in fetal sheep. We isolated mRNA from pituitary, hypothalamus, hippocampus, and brain stem in fetal sheep at 80, 100, 120, 130, and 145 days of gestation and 1 and 7 days after delivery ( n = 4–5/group). Using real-time RT-PCR, we measured mRNA expression levels of glucocorticoid receptor (GR), mineralocorticoid receptor (MR), serum- and glucocorticoid-induced kinase-1 (sgk1), proopiomelanocortin (POMC), CRF, and arginine vasopressin (AVP). Both MR and GR were highly expressed in pituitary and hippocampus; in all tissues GR was more highly expressed than MR. AVP was more highly expressed than CRF in hypothalamus. MR, GR, and sgk1 expression were increased postnatally in brain stem, and sgk1 expression was increased postnatally in hypothalamus. GR expression was reduced in pituitary in term fetuses compared with younger ages. Hypothalamic CRF expression was increased at the end of gestation compared with younger ages, and AVP expression was increased in newborn lambs. Pituitary POMC was increased at 100 days of gestation compared with 80 days; hypothalamic POMC was increased at 120 days. Overall, the results demonstrate the expression of both MR and GR in brain regions important for control of the HPA axis. Decreases in expression of GR in pituitary at the end of gestation might contribute to the decreased corticosteroid negative feedback sensitivity at term in this species.

scholarly journals Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 967 ◽  
Author(s):  
Ting-Li Han ◽  
Yang Yang ◽  
Hua Zhang ◽  
Kai P. Law
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Epidemiological Study ◽  
Internal Standard ◽  
Analytical Techniques ◽  
Experimental Conditions ◽  
Data Set ◽  
Raw Data ◽  
Model Based ◽  
Critical Issues

Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods against each other in an epidemiological study of gestational diabetes using plasma. Results: Our results demonstrated that the quality control-based approaches gave the highest data precision in all methods tested, and would be the method of choice for controlled experimental conditions. But for our epidemiological study, the model-based approaches were able to classify the clinical groups more effectively than the quality control-based approaches because of their ability to minimise not only technical variations, but also biological biases from the raw data. Conclusions: We suggest that metabolomic researchers should optimise and justify the method they have chosen for their experimental condition in order to obtain an optimal biological outcome.

THE ROLE OF MOTION MONITORING IN EXPERIMENTS ON SLIDING FRICTION IN FLAT-ON-FLAT METALLIC CONTACTS

Tribologia ◽  
2017 ◽  
Vol 276 (6) ◽  
pp. 39-44
Author(s):  
Jacek ŁUBIŃSKI
Keyword(s):  
Sliding Friction ◽  
Screening Method ◽  
Vibration Signal ◽  
Analysis Data ◽  
Vibration Measurement ◽  
Global Evaluation ◽  
Data Set ◽  
Metallic Contacts ◽  
Critical Components

The paper describes an example of an experiment on sliding friction of metallic materials in which vibration measurement methods were used to identify friction induced vibrations occurring in sliding. The determination of the parameters of the motion of the critical components in the sliding system in different modes of operation allowed using the vibration signal as a source of information pertaining to the observed process. The tests performed with a metallic material were inspired by the earlier research performed with machine ceramics in which the result of vibration analysis allowed the determination of the correctness of measurement with regard to the conditions of contact and the analyses of the nature of the observed effect of vibration on the inflicted friction. The motion analysis data was used as a basis for a screening method eliminating corrupted measurements from the data set used for global evaluation of the friction characteristics. It was confirmed that, in a steel-on-steel sliding system, similar friction and vibration regimes occur as in ceramics-on-ceramics, but the effects of certain vibration modes are opposite in the two systems, despite the same load/velocity conditions.

Sign in / Sign up

Export Citation Format

Share Document