Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

Soil Contamination Interpretation by the Use of Monitoring Data Analysis

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Contains fulltext : 91843.pdf (publisher's version ) (Open Access)12 p

On the Potential for Interim Storage in Dense Phase CO2 Pipelines

This paper investigates the flexibility that exists within a dense phase carbon dioxide (CO2) pipeline system to accommodate upset conditions in the Carbon Capture and Storage (CCS) network by utilising the pipeline as a storage vessel whilst still maintaining flow into the pipeline. This process is defined in the pipeline industry as “line-packing” and the time available to undertake line-packing is termed the line-packing time. The longer the line-packing time, the more resilient the pipeline system is to flow variations or short term operational issues at the capture or storage site. The aims of the study were; to investigate the impact of typical CO2 pipeline design parameters (diameter, wall thickness and length) as well as CO2 mass flow rate and pipeline inlet and outlet pressure on the available line-packing time and; to derive relationships between the key variables to allow designers to optimise the line-packing time for a pipeline system. The study was undertaken by developing a viable study set of dense phase CO2 pipelines using steady state hydraulic analysis and stress based design principles. The study set was designed to cover the range of design parameters, flow rates and pressures considered to be typical of dense phase pipelines in CCS systems. For each of the pipelines in the study set, the line-packing time was calculated using a transient hydraulic analysis approach. Although by interrogating the results, individual relationships could be identified between key input parameters and the line-packing time, the integration of all of the critical parameters could not be achieved through simple regression analysis techniques. Consequently, using the dataset of pipelines and line-packing times developed, an Artificial Neural Network (ANN) was designed to enable a comprehensive sensitivity analysis of the line-packing time to the input data to be conducted. It is also demonstrated how the ANN can be used as a design tool for the prediction of line-packing time. As would be expected, the line-packing capacity of the pipeline can be increased by increasing the available internal volume of the pipeline, reducing the mass flow rate into the pipeline, increasing the allowable operating stress and managing the inlet pressure and outlet pressures. However, one of the key findings of the work is that, in the dense phase, line-packing times of only up to 8 hours can be achieved for pipeline dimensions typical of those considered for CCS schemes. Consequently it has been confirmed that the pipeline does not represent a long-term storage option for CCS systems. However, if line-packing capability is considered at the design stage then the level of flexibility for the pipeline to act as short-term storage in the network increases. In particular, it is recommended that the effect of increasing the wall thickness on the line-packing time is considered at the design stage to determine the benefits of this option in enabling the pipeline to be used as a short-term storage option in the CCS system and prevent venting of CO2 during short-term outage events at the capture or storage site

Lipidomics Reveals Multiple Pathway Effects of a Multi-Components Preparation on Lipid Biochemistry in ApoE*3Leiden.CETP Mice

Background: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. Methodology/Principal Findings: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p <0.001), CETP levels (-31%,