Biomedical subjects

Alexander Luedemann

Publications and source records attributed to Alexander Luedemann.

4 recordsLinked to original sources

PubMed2005

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Biochemistry↗

PubMed2004

CSB.DB: a comprehensive systems-biology database.

SUMMARY: The open access comprehensive systems-biology database (CSB.DB) presents the results of bio-statistical analyses on gene expression data in association with additional biochemical and physiological knowledge. The main aim of this database platform is to provide tools that support insight into life's complexity pyramid with a special focus on the integration of data from transcript and metabolite profiling experiments. The central part of CSB.DB, which we describe in this applications note, is a set of co-response databases that currently focus on the three key model organisms, Escherichia coli, Saccharomyces cerevisiae and Arabidopsis thaliana. CSB.DB gives easy access to the results of large-scale co-response analyses, which are currently based exclusively on the publicly available compendia of transcript profiles. By scanning for the best co-responses among changing transcript levels, CSB.DB allows to infer hypotheses on the functional interaction of genes. These hypotheses are novel and not accessible through analysis of sequence homology. The database enables the search for pairs of genes and larger units of genes, which are under common transcriptional control. In addition, statistical tools are offered to the user, which allow validation and comparison of those co-responses that were discovered by gene queries performed on the currently available set of pre-selectable datasets. AVAILABILITY: All co-response databases can be accessed through the CSB.DB Web server (http://csbdb.mpimp-golm.mpg.de/).

Database Management Systems↗

PubMed2004

Hypothesis-driven approach to predict transcriptional units from gene expression data.

MOTIVATION: A major issue in computational biology is the reconstruction of functional relationships among genes, for example the definition of regulatory or biochemical pathways. One step towards this aim is the elucidation of transcriptional units, which are characterized by co-responding changes in mRNA expression levels. These units of genes will allow the generation of hypotheses about respective functional interrelationships. Thus, the focus of analysis currently moves from well-established functional assignment through comparison of protein and DNA sequences towards analysis of transcriptional co-response. Tools that allow deducing common control of gene expression have the potential to complement and extend routine BLAST comparisons, because gene function may be inferred from common transcriptional control. RESULTS: We present a co-clustering strategy of genome sequence information and gene expression data, which was applied to identify transcriptional units within diverse compendia of expression profiles. The phenomenon of prokaryotic operons was selected as an ideal test case to generate well-founded hypotheses about transcriptional units. The existence of overlapping and ambiguous operon definitions allowed the investigation of constitutive and conditional expression of transcriptional units in independent gene expression experiments of Escherichia coli. Our approach allowed identification of operons with high accuracy. Furthermore, both constitutive mRNA co-response as well as conditional differences became apparent. Thus, we were able to generate insight into the possible biological relevance of gene co-response. We conclude that the suggested strategy will be amenable in general to the identification of transcriptional units beyond the chosen example of E.coli operons. AVAILABILITY: The analyses of E.coli transcript data presented here are available upon request or at http://csbdb.mpimp-golm.mpg.de/

Algorithms↗

PubMed2003

Parallel analysis of transcript and metabolic profiles: a new approach in systems biology.

The past few years in the medical and biological sciences have been characterized by the advent of systems biology. However, despite the well-known connectivity between the molecules described by transcriptomic, proteomic and metabolomic approaches, few studies have tried to correlate parameters across the various levels of systemic description. When comparing the discriminatory power of metabolic and RNA profiling to distinguish between different potato tuber systems, using the techniques described here suggests that metabolic profiling has a higher resolution than expression profiling. When applying pairwise transcript-metabolite correlation analyses, 571 of the 26,616 possible pairs showed significant correlation, most of which was novel and included several strong correlations to nutritionally important metabolites. We believe this approach to be of high potential value in the identification of candidate genes for modifying the metabolite content of biological systems.

Computational Biology↗