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Biomedical subjects

Ralf Zimmer

Publications and source records attributed to Ralf Zimmer.

32 records · Page 2Linked to original sources
PubMed2004

Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis.

The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.

Cartilage↗
PubMed2004

Functional genomics of osteoarthritis: on the way to evaluate disease hypotheses.

Functional genomics is a challenging new way to address complex diseases such as osteoarthritis on a molecular level. This complements previous research and will open up new areas of so far unrecognized molecular networks. In this respect, articular cartilage is a good target for functional genomics as it contains only one cell type to which all expression signals can be attributed to. Despite considerable limitations at present, such as a low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA-array technology provides a powerful tool to obtain an overview on gene expression patterns hardly achievable with other techniques. This has been shown to be true for known genes as well as for the identification of new genes of interest. Therefore, gene expression analysis will help to identify single genes depending on the disease and experimental conditions investigated. However, the expression pattern of the plethora of expressed genes will paint a picture (network) of disease context, maybe even more pushing forward our understanding of complex diseases such as osteoarthritis.

Cartilage, Articular↗
PubMed2004

Knowledge representation model for systems-level analysis of signal transduction networks.

A Petri-net based model for knowledge representation has been developed to describe as explicitly and formally as possible the molecular mechanisms of cell signaling and their pathological implications. A conceptual framework has been established for reconstructing and analyzing signal transduction networks on the basis of the formal representation. Such a conceptual framework renders it possible to qualitatively understand the cell signaling behavior at systems-level. The mechanisms of the complex signaling network are explored by applying the established framework to the signal transduction induced by potent proinflammatory cytokines, IL-1beta and TNF-alpha The corresponding expert-knowledge network is constructed to evaluate its mechanisms in detail. This strategy should be useful in drug target discovery and its validation.

Computer Graphics↗
PubMed2003

Microarrays: how many do you need?

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.

Computational Biology↗
PubMed2003

Gene expression in chondrocytes assessed with use of microarrays.

BACKGROUND: Despite considerable limitations such as low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA array technology provides a powerful tool with which to obtain an overview of gene expression patterns, hardly achievable with other techniques. This has been shown to be true for the analysis of known genes as well as the discovery of new genes of interest. METHODS: Samples of normal and late-stage osteoarthritic cartilage of human knee joints were analyzed with use of the Human Cancer 1.2 cDNA-array and TaqMan analysis. RESULTS: In spite of a large variability of expression levels among different patients, significant expression patterns for many known genes of interest such as cartilage matrix proteins (e.g., collagen types II, VI, and XI; aggrecan; decorin; biglycan) and matrix-degrading proteases were detected. Of the latter, MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and downregulated in the late disease stages. This indicates that, in the late stages of cartilage degeneration, other degradation pathways might be more important, for example, those involving enzymes such as MMP-2 and MMP-13, both of which were upregulated in late-stage disease. CONCLUSION: Most results have to be considered to be preliminary to a certain degree, as technical tools and interpretation approaches are still emerging and need more validation. Clearly, there is a major challenge to distill information and knowledge out of the obtained mass of data. However, these data will be one basis of a new world of biological understanding. These new insights will be network-based and no longer molecule-centered. Today, molecules have a biochemical and physiological context; tomorrow, biological networks will have molecules as constituents.

Adult↗
PubMed2003

Profile-profile alignment: a powerful tool for protein structure prediction.

The problem of computing the tertiary structure of a protein from a given amino acid sequence has been a major subject of bioinformatics research during the last decade. Many different approaches have been taken to tackle the problem, the most successful of which are based on searching databases to identify a similar amino acid sequence in the PDB and using the corresponding structure as a template for modeling the structure of the query sequence. An important advance for the evaluation of sequence similarity in this context has been the use of a frequency profile that represents a part of the protein sequence space close to the query sequence instead of the query sequence itself. In this paper, we present a further extension of this principle by using profiles instead of the template sequences, also. We show that, by using our newly developed scoring model, the profile-profile alignment approach is able to significantly outperform current state of the art methods like PSI-BLAST, HMMs, or threading methods in a fold recognition setup. This is especially interesting since we show that it holds for closely related sequences as well as for very distantly related ones.

Algorithms↗
PubMed2003

Playing biology's name game: identifying protein names in scientific text.

A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.

Abstracting and Indexing↗
PubMed2002

Confidence measures for protein fold recognition.

MOTIVATION: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. RESULTS: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity--sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable. AVAILABILITY: The benchmark set is available upon request.

Amino Acid Sequence↗
PubMed2002

Co-clustering of biological networks and gene expression data.

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

Algorithms↗
PubMed2002

Functional genomics of osteoarthritis.

Functional genomics is a challenging new way to address a complex disease like osteoarthritis on a molecular level. Despite osteoarthritis being ultimately a biochemical problem, mainly characterized by an imbalanced cartilage matrix turnover, a deeper understanding of molecular events within the tissue cells (i.e., the chondrocytes) will provide not only a better understanding of pathogenetic mechanisms but also new diagnostic markers and cellular targets for therapeutic intervention. This innovative technology represents a challenging approach complementing (not replacing) classical research in previously described and new disease-relevant genes: large-scale functional genomics will open up new areas of so far unrecognized molecular networks. This will include as yet unidentified players in the anabolic-catabolic balance of matrix turnover of articular cartilage as well as disease-relevant intracellular signaling cascades so far hardly investigated in articular chondrocytes. However, care must be taken not to over or misinterpret results and some major challenges must be overcome in order to properly utilize the potential of this technology in the field of osteoarthritis.

Animals↗
PubMed2002

ProML--the protein markup language for specification of protein sequences, structures and families.

We propose a specification language ProML for protein sequences, structures, and families based on the open XML standard. The language allows for portable, system-independent, machine-parsable and human-readable representation of essential features of proteins. The language is of immediate use for several bioinformatics applications: we discuss clustering of proteins into families and the representation of the specific shared features of the respective clusters. Moreover, we use ProML for specification of data used in fold recognition bench-marks exploiting experimentally derived distance constraints.

Programming Languages↗
PubMed2002

Improving fold recognition of protein threading by experimental distance constraints.

We present a comprehensive analysis of methods for improving the fold recognition rate of the threading approach to protein structure prediction by the utilization of few additional distance constraints. The distance constraints between protein residues may be obtained by experiments such as mass spectrometry or NMR spectroscopy. We applied a post-filtering step with new scoring functions incorporating measures of constraint satisfaction to ranking lists of 123D threading alignments. The detailed analysis of the results on a small representative benchmark set show that the fold recognition rate can be improved significantly by up to 30% from about 54%-65% to 77%-84%, approaching the maximal attainable performance of 90% estimated by structural superposition alignments. This gain in performance adds about 10% to the recognition rate already achieved in our previous study with cross-link constraints only. Additional recent results on a larger benchmark set involving a confidence function for threading predictions also indicate notable improvements by our combined approach, which should be particularly valuable for rapid structure determination and validation of protein models.

Nuclear Magnetic Resonance, Biomolecular↗
PubMed2002

A hypergraph-based method for unification of existing protein structure- and sequence-families.

Classification of proteins is a major challenge in bioinformatics. Here an approach is presented, that unifies different existing classifications of protein structures and sequences. Protein structural domains are represented as nodes in a hypergraph. Shared memberships in sequence families result in hyperedges in the graph. The presented method partitions the hypergraph into clusters of structural domains. Each computed cluster is based on a set of shared sequence family memberships. Thus, the clusters put existing protein sequence families into the context of structural family hierarchies. Conversely, structural domains are related to their sequence family memberships, which can be used to gain further knowledge about the respective structural families.

Databases, Protein↗