"Антропологизация" как фактор проблематизации в познании

Рассматривается процесс антропологизации познания через способы проблематизации знания. Делается вывод, что онтологический плюрализм требует соответствующего способа совмещения разных начал, проблематизация которых осуществляется на основе способностей человека познавать мир. Принцип дополнительности и антропный принцип выражают указанные особенности проблематизации знаний человека

Habitat selection models for European wildcat conservation

Populations of the European wildcat (Felis silvestris) are only slowly recovering in Central Europe after a severe decline in the last centuries and require specific conservation plans in many areas. However, detailed information on wildcat occurrence and habitat require- ments is still scarce and controversial. We present a fine-scale habitat selection model for wildcats based on detailed species and land use information and evaluate its accu- racy to predict habitat distribution in new areas. We analysed habitat use within home ranges using single locations of 12 radio-tracked individuals from south western Germany. Several competing models were fitted and compared using generalised linear mixed models (GLMM) and information-theoretic approaches. Radio-tracking data of 9 and 10 wildcats from two distant areas were used to evaluate the models. The selected model predicted habitat associated to close distance to forest, watercourses and mead- ows and a critical distance to villages, single houses and roads. To predict area suitable for home ranges we superimposed rules derived from home range attributes at a higher level of selection. Predictions from the combination of the fine-scale habitat model and home range rules matched well with more than 2000 wildcat observations of south- western Germany. We discuss the application of the model in wildcat conservation for finding potential reintroduction sites, identifying small isolated populations and aiding in the evaluation of the needs of mitigation and compensation within the scope of the European Habitats Directive.Peer Reviewe

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

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Genetical genomics: spotlight on QTL hotspots

No abstract available

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

Nematic liquid crystal alignment on chemical patterns

Patterned Self-Assembled Monolayers (SAMs) promoting both homeotropic and planar degenerate alignment of 6CB and 9CB in their nematic phase, were created using microcontact printing of functionalised organothiols on gold films. The effects of a range of different pattern geometries and sizes were investigated, including stripes, circles and checkerboards. EvanescentWave Ellipsometry was used to study the orientation of the liquid crystal (LC) on these patterned surfaces during the isotropic-nematic phase transition. Pretransitional growth of a homeotropic layer was observed on 1 ¹m homeotropic aligning stripes, followed by a homeotropic mono-domain state prior to the bulk phase transition. Accompanying Monte-Carlo simulations of LCs aligned on nano-patterned surfaces were also performed. These simulations also showed the presence of the homeotropic mono-domain state prior to the transition.</p

Expression QTL Modules as Functional Components Underlying Higher-Order Phenotypes

Systems genetics studies often involve the mapping of numerous regulatory relations between genetic loci and expression traits. These regulatory relations form a bipartite network consisting of genetic loci and expression phenotypes. Modular network organizations may arise from the pleiotropic and polygenic regulation of gene expression. Here we analyzed the expression QTL (eQTL) networks derived from expression genetic data of yeast and mouse liver and found 65 and 98 modules respectively. Computer simulation result showed that such modules rarely occurred in randomized networks with the same number of nodes and edges and same degree distribution. We also found significant within-module functional coherence. The analysis of genetic overlaps and the evidences from biomedical literature have linked some eQTL modules to physiological phenotypes. Functional coherence within the eQTL modules and genetic overlaps between the modules and physiological phenotypes suggests that eQTL modules may act as functional units underlying the higher-order phenotypes