1,755 research outputs found
Characterizing the community structure of complex networks
Community structure is one of the key properties of complex networks and
plays a crucial role in their topology and function. While an impressive amount
of work has been done on the issue of community detection, very little
attention has been so far devoted to the investigation of communities in real
networks. We present a systematic empirical analysis of the statistical
properties of communities in large information, communication, technological,
biological, and social networks. We find that the mesoscopic organization of
networks of the same category is remarkably similar. This is reflected in
several characteristics of community structure, which can be used as
``fingerprints'' of specific network categories. While community size
distributions are always broad, certain categories of networks consist mainly
of tree-like communities, while others have denser modules. Average path
lengths within communities initially grow logarithmically with community size,
but the growth saturates or slows down for communities larger than a
characteristic size. This behaviour is related to the presence of hubs within
communities, whose roles differ across categories. Also the community
embeddedness of nodes, measured in terms of the fraction of links within their
communities, has a characteristic distribution for each category. Our findings
are verified by the use of two fundamentally different community detection
methods.Comment: 15 pages, 20 figures, 4 table
Robustness of circadian clocks to daylight fluctuations: hints from the picoeucaryote Ostreococcus tauri
The development of systemic approaches in biology has put emphasis on
identifying genetic modules whose behavior can be modeled accurately so as to
gain insight into their structure and function. However most gene circuits in a
cell are under control of external signals and thus quantitative agreement
between experimental data and a mathematical model is difficult. Circadian
biology has been one notable exception: quantitative models of the internal
clock that orchestrates biological processes over the 24-hour diurnal cycle
have been constructed for a few organisms, from cyanobacteria to plants and
mammals. In most cases, a complex architecture with interlocked feedback loops
has been evidenced. Here we present first modeling results for the circadian
clock of the green unicellular alga Ostreococcus tauri. Two plant-like clock
genes have been shown to play a central role in Ostreococcus clock. We find
that their expression time profiles can be accurately reproduced by a minimal
model of a two-gene transcriptional feedback loop. Remarkably, best adjustment
of data recorded under light/dark alternation is obtained when assuming that
the oscillator is not coupled to the diurnal cycle. This suggests that coupling
to light is confined to specific time intervals and has no dynamical effect
when the oscillator is entrained by the diurnal cycle. This intringuing
property may reflect a strategy to minimize the impact of fluctuations in
daylight intensity on the core circadian oscillator, a type of perturbation
that has been rarely considered when assessing the robustness of circadian
clocks
ChIPseqR: analysis of ChIP-seq experiments
<p>Abstract</p> <p>Background</p> <p>The use of high-throughput sequencing in combination with chromatin immunoprecipitation (ChIP-seq) has enabled the study of genome-wide protein binding at high resolution. While the amount of data generated from such experiments is steadily increasing, the methods available for their analysis remain limited. Although several algorithms for the analysis of ChIP-seq data have been published they focus almost exclusively on transcription factor studies and are usually not well suited for the analysis of other types of experiments.</p> <p>Results</p> <p>Here we present ChIPseqR, an algorithm for the analysis of nucleosome positioning and histone modification ChIP-seq experiments. The performance of this novel method is studied on short read sequencing data of <it>Arabidopsis thaliana </it>mononucleosomes as well as on simulated data.</p> <p>Conclusions</p> <p>ChIPseqR is shown to improve sensitivity and spatial resolution over existing methods while maintaining high specificity. Further analysis of predicted nucleosomes reveals characteristic patterns in nucleosome sequences and placement.</p
Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli
Coexpression of genes or, more generally, similarity in the expression
profiles poses an unsurmountable obstacle to inferring the gene regulatory
network (GRN) based solely on data from DNA microarray time series. Clustering
of genes with similar expression profiles allows for a course-grained view of
the GRN and a probabilistic determination of the connectivity among the
clusters. We present a model for the temporal evolution of a gene cluster
network which takes into account interactions of gene products with genes and,
through a non-constant degradation rate, with other gene products. The number
of model parameters is reduced by using polynomial functions to interpolate
temporal data points. In this manner, the task of parameter estimation is
reduced to a system of linear algebraic equations, thus making the computation
time shorter by orders of magnitude. To eliminate irrelevant networks, we test
each GRN for stability with respect to parameter variations, and impose
restrictions on its behavior near the steady state. We apply our model and
methods to DNA microarray time series' data collected on Escherichia coli
during glucose-lactose diauxie and infer the most probable cluster network for
different phases of the experiment.Comment: 20 pages, 4 figures; Systems and Synthetic Biology 5 (2011
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Psychosocial work load and stress in the geriatric care
<p>Abstract</p> <p>Background</p> <p>Due to the decrease in informal care by family members and the demographic development, the importance of professional geriatric care will rise considerably. Aim of this study was to investigate the psychosocial workplace situation for employees in this profession.</p> <p>Methods</p> <p>The German version of the COPSOQ (Copenhagen Psychosocial Questionnaire) was used for the assessment of psychosocial factors at work. The instrument includes 22 scales and 3 single items concerning demands, control, stress, support, and strain.</p> <p>Results between two study groups of geriatric care were compared to each other as well as to employees in general hospital care and a general population mean (COPSOQ database).</p> <p>Statistical analysis included t-tests, ANOVA and multiple comparisons of means. Statistical significance (p < 0.01, two-tailed) and a difference of at least 5 points in mean values were defined as the relevant threshold.</p> <p>Results</p> <p>In total 889 respondents from 36 institutions took part in the study. 412 worked in Home Care (HC), 313 in Geriatric Nursing Homes (GNH), 164 in other professions (e.g. administration).</p> <p>Comparison between HC and GNH showed more favourable values for the first group for the most scales, e.g. lower quantitative and emotional demands and less work-privacy conflict, better possibilities for development etc. Compared to external values from the German COPSOQ database for general hospital care (N = 1.195) and the total mean across all professions, COPSOQ-total (N = 11.168), the results are again positive for HC workers on most of the scales concerning demands and social support. The only negative finding is the very low amount of social relations at work due to the obligation to work alone most of the time. Employees in GNH rate predictability, quality of leadership and feedback higher when compared to general hospital care and show some further favourable mean values compared to the COPSOQ mean value for all professions. A disadvantage for GNH is the high rating for job insecurity.</p> <p>A supplementary subgroup analysis showed that the degree of negative evaluation of psychosocial factors concerning demands was related to the amount of working hours per week and the number of on-call duties.</p> <p>Conclusions</p> <p>Compared to employees in general hospital care and the COPSOQ overall mean value across all professions, geriatric care employees and especially home care workers evaluate their psychosocial working situation more positive for most aspects. However, this seems partly due to the very high proportion of part-time workers. Critical results for the two study groups are the relatively high job insecurity in nursing homes and the lack of social relations for the HCrs.</p
Current challenges in software solutions for mass spectrometry-based quantitative proteomics
This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.
Insecticide resistance in Anopheles gambiae from south-western Chad, Central Africa
<p>Abstract</p> <p>Background</p> <p>Indoor residual spraying and insecticide-treated nets (ITN) are essential components of malaria vector control in Africa. Pyrethroids are the only recommended compounds for nets treatment because they are fast-acting insecticides with low mammalian toxicity. However, there is growing concern that pyrethroid resistance may threaten the sustainability of ITN scaling-up programmes. Here, insecticide susceptibility was investigated in <it>Anopheles gambiae </it>sensu lato from an area of large scale ITN distribution programme in south-western Chad.</p> <p>Methods</p> <p>Susceptibility to 4% DDT, 0.05% deltamethrin, 0.75% permethrin, 0.1% bendiocarb and 5% malathion was assessed using the WHO standard procedures for adult mosquitoes. Tests were carried out with two to four days-old, non-engorged female mosquitoes. The <it>An. gambiae </it>Kisumu strain was used as a reference. Knockdown effect was recorded every 5 min and mortality scored 24 h after exposure. Mosquitoes were identified to species and molecular form by PCR-RFLP and genotypes at the <it>kdr </it>locus were determined in surviving specimens by Hot Oligonucleotide Ligation Assay (HOLA).</p> <p>Results</p> <p>During this survey, full susceptibility to malathion was recorded in all samples. Reduced susceptibility to bendiocarb (mortality rate of 96.1%) was found in one sample out of nine assayed. Increased tolerance to pyrethroids was detected in most samples (8/9) with mortality rates ranging from 70.2 to 96.6% for deltamethrin and from 26.7 to 96.3% for permethrin. Pyrethroid tolerance was not associated with a significant increase of knock-down times. <it>Anopheles arabiensis </it>was the predominant species of the <it>An. gambiae </it>complex in the study area, representing 75 to 100% of the samples. Screening for <it>kdr </it>mutations detected the L1014F mutation in 88.6% (N = 35) of surviving <it>An</it>. <it>gambiae </it>sensu stricto S form mosquitoes. All surviving <it>An. arabiensis </it>(N = 49) and M form <it>An</it>. <it>gambiae </it>s.s. (N = 1) carried the susceptible allele.</p> <p>Conclusion</p> <p>This first investigation of malaria vector susceptibility to insecticides in Chad revealed variable levels of resistance to pyrethroid insecticides (permethrin and deltamethrin) in most <it>An</it>. <it>gambiae </it>s.l. populations. Resistance was associated with the L1014F <it>kdr </it>mutation in the S form of <it>An. gambiae </it>s.s.. Alternative mechanisms, probably of metabolic origin are involved in <it>An. arabiensis</it>. These results emphasize the crucial need for insecticide resistance monitoring and in-depth investigation of resistance mechanisms in malaria vectors in Chad. The impact of reduced susceptibility to pyrethroids on ITN efficacy should be further assessed.</p
A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB
Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions
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