@phdthesis{Nilla2012,
  author    = {Nilla, Jaya Santosh Chakravarthy},
  title     = {An Integrated Knowledgebase and Network Analysis Applied on Platelets and Other Cell Types},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-85730},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Systems biology looks for emergent system effects from large scale assemblies of molecules and data, for instance in the human platelets. However, the computational efforts in all steps before such insights are possible can hardly be under estimated. In practice this involves numerous programming tasks, the establishment of new database systems but as well their maintenance, curation and data validation. Furthermore, network insights are only possible if strong algorithms decipher the interactions, decoding the hidden system effects. This thesis and my work are all about these challenges. To answer this requirement, an integrated platelet network, PlateletWeb, was assembled from different sources and further analyzed for signaling in a systems biological manner including multilevel data integration and visualization. PlateletWeb is an integrated network database and was established by combining the data from recent platelet proteome and transcriptome (SAGE) studies. The information on protein-protein interactions and kinase-substrate relationships extracted from bioinformatical databases as well as published literature were added to this resource. Moreover, the mass spectrometry-based platelet phosphoproteome was combined with site-specific phosphorylation/ dephosphorylation information and then enhanced with data from Phosphosite and complemented by bioinformatical sequence analysis for site-specific kinase predictions. The number of catalogued platelet proteins was increased by over 80\% as compared to the previous version. The integration of annotations on kinases, protein domains, transmembrane regions, Gene Ontology, disease associations and drug targets provides ample functional tools for platelet signaling analysis. The PlateletWeb resource provides a novel systems biological workbench for the analysis of platelet signaling in the functional context of protein networks. By comprehensive exploration, over 15000 phosphorylation sites were found, out of which 2500 have the corresponding kinase associations. The network motifs were also investigated in this anucleate cell and characterize signaling modules based on integrated information on phosphorylation and protein-protein interactions. Furthermore, many algorithmic approaches have been introduced, including an exact approach (heinz) based on integer linear programming. At the same time, the concept of semantic similarities between two genes using Gene Ontology (GO) annotations has become an important basis for many analytical approaches in bioinformatics. Assuming that a higher number of semantically similar gene functional annotations reflect biologically more relevant interactions, an edge score was devised for functional network analysis. Bringing these two approaches together, the edge score, based on the GO similarity, and the node score, based on the expression of the proteins in the analyzed cell type (e.g. data from proteomic studies), the functional module as a maximum-scoring sub network in large protein-protein interaction networks was identified. This method was applied to various proteome datasets (different types of blood cells, embryonic stem cells) to identify protein modules that functionally characterize the respective cell type. This scalable method allows a smooth integration of data from various sources and retrieves biologically relevant signaling modules.},
  subject      = {Systembiologie},
  language  = {en}
}
@article{JahnSchrammSchnoelzeretal.2012,
  author    = {Jahn, Daniel and Schramm, Sabine and Schn{\"o}lzer, Martina and Heilmann, Clemens J. and de Koster, Chris G. and Sch{\"u}tz, Wolfgang and Benavente, Ricardo and Alsheimer, Manfred},
  title     = {A truncated lamin A in the Lmna\(^{-/-}\) mouse line: Implications for the understanding of laminopathies},
  series = {Nucleus},
  volume    = {3},
  journal   = {Nucleus},
  number    = {5},
  doi       = {10.4161/nucl.21676},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-127281},
  pages     = {463-474},
  year      = {2012},
  abstract  = {During recent years a number of severe clinical syndromes, collectively termed laminopathies, turned out to be caused by various, distinct mutations in the human LMNA gene. Arising from this, remarkable progress has been made to unravel the molecular pathophysiology underlying these disorders. A great benefit in this context was the generation of an A-type lamin deficient mouse line (Lmna\(^{-/-}\)) by Sullivan and others,1 which has become one of the most frequently used models in the field and provided profound insights to many different aspects of A-type lamin function. Here, we report the unexpected finding that these mice express a truncated Lmna gene product on both transcriptional and protein level. Combining different approaches including mass spectrometry, we precisely define this product as a C-terminally truncated lamin A mutant that lacks domains important for protein interactions and post-translational processing. Based on our findings we discuss implications for the interpretation of previous studies using Lmna\(^{-/-}\) mice and the concept of human laminopathies.},
  language  = {en}
}
@article{WeisseHeddergottHeydtetal.2012,
  author    = {Weiße, Sebastian and Heddergott, Niko and Heydt, Matthias and Pfl{\"a}sterer, Daniel and Maier, Timo and Haraszti, Tamas and Grunze, Michael and Engstler, Markus and Rosenhahn, Axel},
  title     = {A Quantitative 3D Motility Analysis of Trypanosoma brucei by Use of Digital In-line Holographic Microscopy},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {5},
  doi       = {10.1371/journal.pone.0037296},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130666},
  pages     = {e37296},
  year      = {2012},
  abstract  = {We present a quantitative 3D analysis of the motility of the blood parasite Trypanosoma brucei. Digital in-line holographic microscopy has been used to track single cells with high temporal and spatial accuracy to obtain quantitative data on their behavior. Comparing bloodstream form and insect form trypanosomes as well as mutant and wildtype cells under varying external conditions we were able to derive a general two-state-run-and-tumble-model for trypanosome motility. Differences in the motility of distinct strains indicate that adaption of the trypanosomes to their natural environments involves a change in their mode of swimming.},
  language  = {en}
}
@article{StaigerCadotKooteretal.2012,
  author    = {Staiger, Christine and Cadot, Sidney and Kooter, Raul and Dittrich, Marcus and M{\"u}ller, Tobias and Klau, Gunnar W. and Wessels, Lodewyk F. A.},
  title     = {A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {4},
  doi       = {10.1371/journal.pone.0034796},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-131323},
  pages     = {e34796},
  year      = {2012},
  abstract  = {Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.},
  language  = {en}
}