Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

Localization and variable expression of Gαi2 in human endometrium and Fallopian tubes

Background: Heterotrimeric G proteins take part in membrane-mediated cell signalling and have a role in hormonal regulation. This study clarifies the expression and localization of the G protein subunit Gαi2 in the human endometrium and Fallopian tube and changes in Gαi2 expression in human endometrium during the menstrual cycle. Methods: The expression of Gαi2 was identified by Polymerase chain reaction (PCR), and localization confirmed by immunostaining. Cyclic changes in Gαi2 expression during the menstrual cycle were evaluated by quantitative real-time PCR. Results: We found Gαi2 to be expressed in human endometrium, Fallopian tube tissue and in primary cultures of Fallopian tube epithelial cells. Our studies revealed enriched localization of Gαi2 in Fallopian tube cilia and in endometrial glands. We showed that Gαi2 expression in human endometrium changes significantly during the menstrual cycle, with a higher level in the secretory versus proliferative and menstrual phases (P < 0.05). Conclusions: Gαi2 is specifically localized in human Fallopian tube epithelial cells, particularly in the cilia, and is likely to have a cilia-specific role in reproduction. Significantly variable expression of Gαi2 during the menstrual cycle suggests Gαi2 might be under hormonal regulation in the female reproductive tract in vivo. © 2007 Oxford University Press.postprin

FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data

BACKGROUND: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process. RESULTS: The clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License. CONCLUSION: The FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis

Occupational, domestic and environmental mesothelioma risks in the British population: a case–control study

We obtained lifetime occupational and residential histories by telephone interview with 622 mesothelioma patients (512 men, 110 women) and 1420 population controls. Odds ratios (ORs) were converted to lifetime risk (LR) estimates for Britons born in the 1940s. Male ORs (95% confidence interval (CI)) relative to low-risk occupations for >10 years of exposure before the age of 30 years were 50.0 (25.8–96.8) for carpenters (LR 1 in 17), 17.1 (10.3–28.3) for plumbers, electricians and painters, 7.0 (3.2–15.2) for other construction workers, 15.3 (9.0–26.2) for other recognised high-risk occupations and 5.2 (3.1–8.5) in other industries where asbestos may be encountered. The LR was similar in apparently unexposed men and women (∼1 in 1000), and this was approximately doubled in exposed workers' relatives (OR 2.0, 95% CI 1.3–3.2). No other environmental hazards were identified. In all, 14% of male and 62% of female cases were not attributable to occupational or domestic asbestos exposure. Approximately half of the male cases were construction workers, and only four had worked for more than 5 years in asbestos product manufacture

Predicting the Lay Preventive Strategies in Response to Avian Influenza from Perceptions of the Threat

Background: The identification of patterns of behaviors that lay people would engage in to protect themselves from the risk of infection in the case of avian influenza outbreak, as well as the lay perceptions of the threat that underlie these risk reduction strategies. Methodology/Principal Findings: A population-based survey (N = 1003) was conducted in 2008 to understand and describe how the French public might respond to a possible outbreak. Factor analyses highlighted three main categories of risk reduction strategies consisting of food quality assurance, food avoidance, and animal avoidance. In combination with the fear of contracting avian influenza, mental representations associated with the manifestation and/or transmission of the disease were found to significantly and systematically shape the behavioral responses to the perceived threat. Conclusions/Significance: This survey provides insight into the nature and predictors of the protective patterns that might be expected from the general public during a novel domestic outbreak of avian influenza

A Genetic Risk Score Combining Ten Psoriasis Risk Loci Improves Disease Prediction

Psoriasis is a chronic, immune-mediated skin disease affecting 2–3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS) combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS) and a weighted (wGRS) approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7) versus 12.09 (SD 1.8), p = 4.577×10−40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63–14.57), p = 2.010×10−65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC). The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10−8). Additionally, the AUC for HLA-C alone (rs10484554) was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18), highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10−6) and family history (p = 0.020). Using a liability threshold model, we estimated that the 10 risk loci account for only11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date