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Beisser D.,University of Wurzburg | Brunkhorst S.,University of Wurzburg | Dandekar T.,University of Wurzburg | Klau G.W.,Life science Group | And 3 more authors.
Bioinformatics | Year: 2012

Motivation: High-throughput molecular data provide a wealth of information in ththat can be integrated into network analysis. Several approaches exist that identify functional modulese context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions.Results: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia. © The Author 2012. Published by Oxford University Press. All rights reserved. Source


Marino S.,University of Michigan | El-Kebir M.,VU University Amsterdam | El-Kebir M.,Life science Group | Kirschner D.,University of Michigan
Journal of Theoretical Biology | Year: 2011

Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host-mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can be regulated by key immune cell migration rates. Our hybrid multi-organ implementation suggests that effector CD4+ T cells can rescue the system from a persistent infection and lead to clearance once a granuloma is fully formed. This could be effective as an immunotherapy strategy for latently infected individuals. © 2011 Elsevier Ltd. Source


Reimers A.C.,Life science Group
PLoS ONE | Year: 2015

Background Sampling methods have proven to be a very efficient and intuitive method to understand properties of complicated spaces that cannot easily be computed using deterministic methods. Therefore, sampling methods became a popular tool in the applied sciences. Results Here, we show that sampling methods are not an appropriate tool to analyze qualitative properties of complicated spaces unless RP = NP.We illustrate these results on the example of the thermodynamically feasible flux space of genome-scale metabolic networks and show that with artificial centering hit and run (ACHR) not all reactions that can have variable flux rates are sampled with variables flux rates. In particular a uniform sample of the flux space would not sample the flux variabilities completely. Conclusion We conclude that unless theoretical convergence results exist, qualitative results obtained from sampling methods should be considered with caution and if possible double checked using a deterministic method. Copyright: © 2015 Arne C. Reimers. Source


Rymarquis L.A.,University of Delaware | Souret F.F.,University of Delaware | Souret F.F.,Life science Group | Green P.J.,University of Delaware
RNA | Year: 2011

One of the major players controlling RNA decay is the cytoplasmic 5′-to-3′ exoribonuclease, which is conserved among eukaryotic organisms. In Arabidopsis, the 5′-to-3′ exoribonuclease XRN4 is involved in disease resistance, the response to ethylene, RNAi, and miRNA-mediated RNA decay. Curiously, XRN4 appears to display selectivity among its substrates because certain 3′ cleavage products formed by miRNA-mediated decay, such as from ARF10 mRNA, accumulate in the xrn4 mutant, whereas others, such as from AGO1, do not. To examine the nature of this selectivity, transcripts that differentially accumulate in xrn4 were identified by combining PARE and Affymetrix arrays. Certain functional categories, such as stamen-associated proteins and hydrolases, were over-represented among transcripts decreased in xrn4, whereas transcripts encoding nuclear-encoded chloroplast-targeted proteins and nucleic acid-binding proteins were over-represented in transcripts increased in xrn4. To ascertain if RNA sequence influences the apparent XRN4 selectivity, a series of chimeric constructs was generated in which the miRNA-complementary sites and different portions of the surrounding sequences from AGO1 and ARF10 were interchanged. Analysis of the resulting transgenic plants revealed that the presence of a 150 nucleotide sequence downstream from the ARF10 miRNA-complementary site conferred strong accumulation of the 3′ cleavage products in xrn4. In addition, sequence analysis of differentially accumulating transcripts led to the identification of 27 hexamer motifs that were over-represented in transcripts or miRNA-cleavage products accumulating in xrn4. Taken together, the data indicate that specific mRNA sequences, like those in ARF10, and mRNAs from select functional categories are attractive targets for XRN4-mediated decay. Published by Cold Spring Harbor Laboratory Press. Copyright © 2011 RNA Society. Source


Beisser D.,University of Wurzburg | Klau G.W.,Life science Group | Dandekar T.,University of Wurzburg | Muller T.,University of Wurzburg | Dittrich M.T.,University of Wurzburg
Bioinformatics | Year: 2010

Motivation: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. Results: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. Availability: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de. Contact: marcus.dittrich@biozentrum.uni-wuerzburg.de; tobias.mueller@biozentrum.uni-wuerzburg.de. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org. Source

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