What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Pharmacy Practice in the Prostějov Region during the Health Care Development Process after the 1989

Pharmacy Practice in the Prostějov Region during the Health Care Development Process after the 1989 Student: Eva Navrátilová Tutor: Doc. PhDr. František Dohnal, CSc. Social and Clinical Pharmacy Department Pharmaceutical Faculty in Hradec Králové The Charles University in Prague, Czech Republic Theme: Pharmacy Practice in the Prostějov Region during the Health Care Development Process after the 1989 Objective: Survey of the history of pharmacies and pharmacists in the Prostějov Region after the 1989 during the health care development process. Methods: Excerption of the availability literature, processing the regional newspapers that were obtained from the Town library in Prostějov and Town archive in Prostějov, the questionnaires, the materials of the district corporation of the pharmacists and interviews with the local pharmacy keepers. Results: Before 1989 in the Prostějov Region there were 12 pharmacies, 6 in the region and 6 in the city of Prostějov. After the 1989 many new pharmacies were established. Some pharmacies have established the OOVL (a small pharmacy by their "mother pharmacy"), their expansion have begun after the 1998. In the city of Prostějov we can find the pharmacies in the supermarkets too. Some historical pharmacies and some new have disappeared because of their competition. In..