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LINUX Cluster Project

Systembiologie der Pflanzen (Sysbio)


  • Name: Lehrstuhl für Systembiologie der Pflanzen
  • Address: Emil-Ramann-Str. 8, 85354 Freising
  • Project Proposal Date: 2017-02-19 14:34:21


ChIP seq data analysis (Emmanouil Bastakis, 100h CPU per month) ChIP seq data analysis (mapping on the reference genome, peak identification, motif finding and GO analysis).Goal of this project is to recognise all the potential binding sites of the transcription factor which I'm stunding, in the whole genome of Arabidopsis thaliana. By this, I will be able to elucidate the regulatory function of this transcription factor for a big number of genes. This data analysis will allow me later on to constract a regulatory network of genes which are controled by the transcription factor which I am interesting for. Better understand of the functions of this protein it will shed light in the fundumental function of the plant like flowering time, germination and greening. Brassicaceae PPI network comparison (Yang Jae Kang, 300 * 50 = 15000 hours per month) Drought and salination of farm land threaten the capacity to feed a growing world population at times of global warming. Plants respond to desiccation stress by activation of signaling, reduction of metabolic activity, and other physiological adjustments. However, the main model for molecular studies, Arabidopsis thaliana, is sensitive to most abiotic stresses and therefore less than ideal to elucidate the mechanisms of stress tolerance. Building on recent advances in mapping, evaluating and analyzing interactome networks, this project is to chart the desiccation stress networks for four closely related brassicaceae: Arabidopsis thaliana (drought sensitive), A. lyrata (drought tolerant), A. halleri (drought sensitive) and Eutrema salsugineum (drought, salt tolerant). In addition to static interactome network mapping, dynamic stress response signaling will be analysed. Systems biology of Chlamydomonas reinhardtii metabolism (Gurudutta Panda, 240 * 32 = 7680 hours per month) In the quest for alternative energies the unicellular algae Chlamydomonas reinhardtii (hereafter: Chlamydomonas) has gained significant attention in recent years as it can use photosynthetic energy to produce molecular hydrogen (H2) and generate biomass in a manner that does not compete with agricultural processes. Moreover, Chlamydomonas is a widely used model to study basic plant processes, such as photosynthesis, and even human disease processes. The biotechnological potential has motivated the development of initial quantitative models of Chlamydomonas metabolism to obtain a detailed quantitative understanding of the metabolic and regulatory processes that would subsequently allow a rational manipulation of algae towards increased biomass or H2 yields. However, current metabolic models are limited in their predictive power and utility, largely due to missing functional information and experimental data about the biochemical activity, regulation, and localization of enzymes. Protein interactions play a critical role in all aspects of cellular biology and have important roles in structuring and regulating metabolic processes. In metabolism, it has been shown that many sequential enzymes in a given pathway either directly interact, or are linked by common interaction partners. Thus biological knowledge in form of physical interactions can be used to develop hypotheses on the pathways and specificities of uncharacterized enzymes and transporters. In this project, we propose to perform a systematic characterization of the Chlamydomonas protein-protein interactome and its metabolic network. Topology and Ontology-based network analysis of the interactome network (9,000 nodes and >15,000 interactions) and metabolic network (2500 nodes and 6000 interactions) will be carried out. Furthermore, the metabolic network will also be analysed using elementary mode and extreme pathway analysis, which will be computationally intensive given the vast search space of elementary modes present in a metabolic network. Transcriptome profiling (analysis of RNA-Sequencing datasets) and generation of gene co-expression network will also be performed. De Novo assembly of the short sequences obtained from RNA-Sequencing experiments would require higher computational powers (32 cores, storage space >2TB, RAM >64 GB) mostly for thieoir assembly on to the genome. The project is focused on metabolic enzymes, transporters, and signaling proteins with the aim to obtain a better representation of the metabolic network. The hypotheses derived from the interactome analysis will be used to refine existing quantitative models of Chlamydomonas metabolism. Hypotheses on new reactions derived from the network analysis, that prove to have the largest quantitative impact on fluxes and their regulation during H2 production will be experimentally validated. Determination of adaption to environment (Stefan Altmann, ~13500h (27,000 genes * 30min) Phytohormones play essential roles in developmental decisions and stress responses. While many phytohormone signal transduction proteins have been identified over the past decades, a systems perspective is still missing and many aspects of mediating signal transduction and crosstalk remain to be discovered. The goal of this project is to analyze phytohormone signaling from a systems perspective by systematically mapping the interaction network of known and likely signaling proteins. To identify possible adaptions to the environment of the of the Arabidopsis natural accessions different selection measures like Tajima’s D, the McDonald-Kreitman Test and several others should be calculated. Therefor the genomic sequences of 1135 accessions are used to calculate these measures for over 27000 genes and compare these measures against samples generated under neutral selection to determine the significance of the results.