Biomedical subjects

R Steuer

Publications and source records attributed to R Steuer.

6 recordsLinked to original sources

PubMed2005

Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.

Algorithms↗

PubMed2003

Observing and interpreting correlations in metabolomic networks.

MOTIVATION: Metabolite profiling aims at an unbiased identification and quantification of all the metabolites present in a biological sample. Based on their pair-wise correlations, the data obtained from metabolomic experiments are organized into metabolic correlation networks and the key challenge is to deduce unknown pathways based on the observed correlations. However, the data generated is fundamentally different from traditional biological measurements and thus the analysis is often restricted to rather pragmatic approaches, such as data mining tools, to discriminate between different metabolic phenotypes. METHODS AND RESULTS: We investigate to what extent the data generated networks reflect the structure of the underlying biochemical pathways. The purpose of this work is 2-fold: Based on the theory of stochastic systems, we first introduce a framework which shows that the emergent correlations can be interpreted as a 'fingerprint' of the underlying biophysical system. This result leads to a systematic relationship between observed correlation networks and the underlying biochemical pathways. In a second step, we investigate to what extent our result is applicable to the problem of reverse engineering, i.e. to recover the underlying enzymatic reaction network from data. The implications of our findings for other bioinformatics approaches are discussed.

Algorithms↗

PubMed2003

Interpreting correlations in metabolomic networks.

Correlations, as observed between the concentrations of metabolites in a biological sample, may be used to gain additional information about the physiological state of a given tissue. In this mini-review, we discuss the integration of these observed correlations into metabolomic networks and their relationships with the underlying biochemical pathways.

Biochemical Phenomena↗

PubMed2002

The mutual information: detecting and evaluating dependencies between variables.

MOTIVATION: Clustering co-expressed genes usually requires the definition of 'distance' or 'similarity' between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data. RESULTS: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.

Algorithms↗

PubMed2001

Entropy and local uncertainty of data from sensory neurons.

We present an empirical comparison between neural interspike interval sequences obtained from two different kinds of sensory receptors. Both differ in their internal structure as well as in the strength of correlations and the degree of predictability found in the respective spike trains. As a further tool in this context, we suggest the local uncertainty, assigning a well-defined predictability to individual spikes. The local uncertainty is demonstrated to reveal significant patterns within the interspike interval sequences, even when its overall structure is (almost) random. Our approach is based on the concept of symbolic dynamics and information theory.

Animals↗

PubMedDate not supplied

Factors affecting satisfaction among community-based hospice volunteer visitors.

Trained volunteers are an essential component in the delivery of care to clients and families facing a terminal and/or life-threatening illness. As the need for hospice care increases, so does the need to increase the number of volunteers available for visiting. Hospice of London, which is a community based hospice, proposed that volunteers who felt satisfied would remain with the organization longer, thereby, decreasing the costs associated with training new volunteers and enhancing the ability of the agency to provide high-quality volunteer client matches. Accordingly, a survey was conducted in August 1992 to determine which factors were related to hospice volunteer satisfaction. One hundred and five volunteer were surveyed over the telephone. Results demonstrated a positive correlation between satisfaction and feeling like a team member, receiving feedback from staff, feeling valuable and having the volunteer's expectations match the position. Differences in the factors related to satisfaction were noted when the groups were divided by age and gender.

Community Health Services↗