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Results for “network analysis”

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At least 307 records · Page 17Linked to original sources
PubMed2000

Metabolic network analysis of Penicillium chrysogenum using (13)C-labeled glucose.

Using (13)C-labeled glucose fed to a penicillin-overproducing strain of Penicillium chrysogenum, the intracellular fluxes were quantified, and the presence of two new pathways, not previously described in this organism, is suggested. Thus, glycine was synthesized not only by serine hydroxymethyltransferase, but also by threonine aldolase. The formation of cytosolic acetyl-CoA was found to be synthesized both via the citrate lyase-catalyzed reaction and by degradation of the penicillin side-chain precursor, phenoxyacetic acid. Furthermore, the experimental data indicate that the main activities of homocitrate synthase and alpha-isopropylmalate synthase are located in the cytosol. All experimental data on the labeling patterns were obtained using gas chromatography-mass spectrometry, which is faster and more sensitive than the nuclear magnetic resonance methods usually applied for analysis of labeling patterns.

Acetyl Coenzyme A↗
PubMed2004

Multi-knockout genetic network analysis: the Rad6 example.

A novel and rigorous Multi-perturbation Shapley Value Analysis (MSA) method has been recently presented [12]. The method addresses the challenge of defining and calculating the functional causal contributions of elements of a biological system. This paper presents the first study applying MSA to the analysis of gene knockout data. The MSA identifies the importance of genes in the Rad6 DNA repair pathway of the yeast S. cerevisiae, quantifying their contributions and characterizing their functional interactions. Incorporating additional biological knowledge, a new functional description of the Rad6 pathway is provided, predicting the existence of additional DNA polymerase and RFC-like complexes. The MSA is the first method for rigorously analyzing multi-knockout experiments, which are likely to soon become a standard and necessary tool for analyzing complex biological systems.

Algorithms↗
PubMed1997

Geographic information systems, spatial network analysis, and contraceptive choice.

How does family planning accessibility affect contraceptive choice? In this paper we use techniques of spatial analysis to develop measures of family planning accessibility, and evaluate the effects of these geographically derived measures in a multilevel statistical model of temporary method choice in Nang Rong, Thailand. In our analyses we combine spatial data obtained from maps and Global Positioning System (GPS) readings with sociodemographic data from surveys and administrative records. The new measures reveal (1) important travel time effects even when family planning outlets are close by; (2) independent effects of road composition; (3) the relevance of alternative sources of family planning supply; and (4) the importance of the local history of program placement.

Choice Behavior↗
PubMed2006

Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.

BACKGROUND: Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. RESULTS: We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome--the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. CONCLUSION: The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest.

Algorithms↗
PubMed2025

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Humans↗
PubMed2004

Genomic analysis of regulatory network dynamics reveals large topological changes.

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Algorithms↗
PubMed1997

Strychnine analysis with neuronal networks in vitro: extracellular array recording of network responses.

Neurons, by virtue of intrinsic electrophysiological mechanisms, represent transducers that report the dynamics of cell death, receptor-ligand interactions, alterations in metabolism and generic membrane perforation processes. In cell culture, mammalian neurons form fault-tolerant, spontaneously active systems with great sensitivity to their chemical environment and generate response profiles that are often concentration- and substance-specific. Changes in action potential patterns are usually detected before morphological changes and cell damage occur. This provides sensitivity and reversibility. Such biological systems may be used to screen rapidly for novel pharmacological substances, toxic agents and certain odorants. This paper reports on substance-dependent major changes in spontaneous native activity patterns by the synaptically active (glycine receptor blocker) strychnine. Via 64-channel array recordings of spontaneously active murine spinal cord cell cultures, increased multichannel bursting at 5-20 nM strychnine and regular, coordinated bursting above 5 microM could be reliably generated. By artificial neural network analysis a quantitative correlation of network signals and strychnine concentration could be evaluated for small concentrations of strychnine. The results indicate that cultured neuronal networks already represent reliable and practical systems which can be used for the detection of chemical substances and the characterization of their biological influences.

Animals↗