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  • 1
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    American Association for the Advancement of Science (AAAS)
    Publication Date: 2007-04-28
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sauer, Uwe -- Heinemann, Matthias -- Zamboni, Nicola -- New York, N.Y. -- Science. 2007 Apr 27;316(5824):550-1.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute of Molecular Systems Biology, ETH Zurich, Switzerland. sauer@ethz.ch〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17463274" target="_blank"〉PubMed〈/a〉
    Keywords: Computational Biology ; Escherichia coli/*genetics/growth & development/*metabolism ; Escherichia coli Proteins/genetics/metabolism ; Genes, Bacterial ; *Metabolic Networks and Pathways/genetics ; Mutation ; *Proteome ; RNA, Bacterial/genetics/metabolism ; RNA, Messenger/genetics/metabolism ; Systems Biology/*methods ; *Transcription, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2012-05-05
    Description: Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be measured by (13)C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of (13)C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms' environmental context.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Schuetz, Robert -- Zamboni, Nicola -- Zampieri, Mattia -- Heinemann, Matthias -- Sauer, Uwe -- New York, N.Y. -- Science. 2012 May 4;336(6081):601-4. doi: 10.1126/science.1216882.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute of Molecular Systems Biology, Eidgenossische Technische Hochschule Zurich, Zurich, Switzerland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22556256" target="_blank"〉PubMed〈/a〉
    Keywords: Adaptation, Physiological ; Adenosine Triphosphate/metabolism ; Aerobiosis ; Algorithms ; Bacteria/growth & development/*metabolism ; *Biological Evolution ; Biomass ; Computer Simulation ; Escherichia coli/genetics/growth & development/*metabolism ; Glucose/metabolism ; *Metabolic Networks and Pathways ; Models, Biological
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2012-03-03
    Description: Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Buescher, Joerg Martin -- Liebermeister, Wolfram -- Jules, Matthieu -- Uhr, Markus -- Muntel, Jan -- Botella, Eric -- Hessling, Bernd -- Kleijn, Roelco Jacobus -- Le Chat, Ludovic -- Lecointe, Francois -- Mader, Ulrike -- Nicolas, Pierre -- Piersma, Sjouke -- Rugheimer, Frank -- Becher, Dorte -- Bessieres, Philippe -- Bidnenko, Elena -- Denham, Emma L -- Dervyn, Etienne -- Devine, Kevin M -- Doherty, Geoff -- Drulhe, Samuel -- Felicori, Liza -- Fogg, Mark J -- Goelzer, Anne -- Hansen, Annette -- Harwood, Colin R -- Hecker, Michael -- Hubner, Sebastian -- Hultschig, Claus -- Jarmer, Hanne -- Klipp, Edda -- Leduc, Aurelie -- Lewis, Peter -- Molina, Frank -- Noirot, Philippe -- Peres, Sabine -- Pigeonneau, Nathalie -- Pohl, Susanne -- Rasmussen, Simon -- Rinn, Bernd -- Schaffer, Marc -- Schnidder, Julian -- Schwikowski, Benno -- Van Dijl, Jan Maarten -- Veiga, Patrick -- Walsh, Sean -- Wilkinson, Anthony J -- Stelling, Jorg -- Aymerich, Stephane -- Sauer, Uwe -- New York, N.Y. -- Science. 2012 Mar 2;335(6072):1099-103. doi: 10.1126/science.1206871.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22383848" target="_blank"〉PubMed〈/a〉
    Keywords: *Adaptation, Physiological ; Algorithms ; Bacillus subtilis/*genetics/*metabolism ; Bacterial Proteins/metabolism ; Computer Simulation ; Data Interpretation, Statistical ; Gene Expression Regulation, Bacterial ; *Gene Regulatory Networks ; Genome, Bacterial ; Glucose/*metabolism ; Malates/*metabolism ; Metabolic Networks and Pathways/*genetics ; Metabolome ; Metabolomics ; Models, Biological ; Operon ; Promoter Regions, Genetic ; Transcription Factors/metabolism ; Transcription, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
    Publication Date: 2013-02-23
    Description: The Ser-Thr kinase mammalian target of rapamycin (mTOR) controls cell growth and metabolism by stimulating glycolysis and synthesis of proteins and lipids. To further understand the central role of mTOR in cell physiology, we used quantitative phosphoproteomics to identify substrates or downstream effectors of the two mTOR complexes. mTOR controlled the phosphorylation of 335 proteins, including CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase). CAD catalyzes the first three steps in de novo pyrimidine synthesis. mTORC1 indirectly phosphorylated CAD-S1859 through S6 kinase (S6K). CAD-S1859 phosphorylation promoted CAD oligomerization and thereby stimulated de novo synthesis of pyrimidines and progression through S phase of the cell cycle in mammalian cells. Thus, mTORC1 also stimulates the synthesis of nucleotides to control cell proliferation.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Robitaille, Aaron M -- Christen, Stefan -- Shimobayashi, Mitsugu -- Cornu, Marion -- Fava, Luca L -- Moes, Suzette -- Prescianotto-Baschong, Cristina -- Sauer, Uwe -- Jenoe, Paul -- Hall, Michael N -- New York, N.Y. -- Science. 2013 Mar 15;339(6125):1320-3. doi: 10.1126/science.1228771. Epub 2013 Feb 21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Biozentrum, University of Basel, Basel, Switzerland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23429704" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Aspartate Carbamoyltransferase/genetics/*metabolism ; Carbamoyl-Phosphate Synthase (Glutamine-Hydrolyzing)/genetics/*metabolism ; Cells, Cultured ; Dihydroorotase/genetics/*metabolism ; HeLa Cells ; Humans ; Mice ; Mice, Knockout ; Multiprotein Complexes/*metabolism ; Phosphoproteins/*metabolism ; Proteome/metabolism ; Pyrimidines/*biosynthesis ; TOR Serine-Threonine Kinases/*metabolism
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 5
    Publication Date: 2016-03-19
    Description: Postnatal colonization of the body with microbes is assumed to be the main stimulus to postnatal immune development. By transiently colonizing pregnant female mice, we show that the maternal microbiota shapes the immune system of the offspring. Gestational colonization increases intestinal group 3 innate lymphoid cells and F4/80(+)CD11c(+) mononuclear cells in the pups. Maternal colonization reprograms intestinal transcriptional profiles of the offspring, including increased expression of genes encoding epithelial antibacterial peptides and metabolism of microbial molecules. Some of these effects are dependent on maternal antibodies that potentially retain microbial molecules and transmit them to the offspring during pregnancy and in milk. Pups born to mothers transiently colonized in pregnancy are better able to avoid inflammatory responses to microbial molecules and penetration of intestinal microbes.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gomez de Aguero, Mercedes -- Ganal-Vonarburg, Stephanie C -- Fuhrer, Tobias -- Rupp, Sandra -- Uchimura, Yasuhiro -- Li, Hai -- Steinert, Anna -- Heikenwalder, Mathias -- Hapfelmeier, Siegfried -- Sauer, Uwe -- McCoy, Kathy D -- Macpherson, Andrew J -- New York, N.Y. -- Science. 2016 Mar 18;351(6279):1296-302. doi: 10.1126/science.aad2571.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Maurice Muller Laboratories (DKF), Universitatsklinik fur Viszerale Chirurgie und Medizin Inselspital, Murtenstrasse 35, University of Bern, 3010 Bern, Switzerland. ; Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH) Zurich, 8093 Zurich, Switzerland. ; Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. ; Institute for Infectious Diseases, University of Bern, 3010 Bern, Switzerland. ; Maurice Muller Laboratories (DKF), Universitatsklinik fur Viszerale Chirurgie und Medizin Inselspital, Murtenstrasse 35, University of Bern, 3010 Bern, Switzerland. andrew.macpherson@insel.ch.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26989247" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Antibodies/immunology ; Escherichia coli/immunology ; Female ; Gastrointestinal Microbiome/*immunology ; Germ-Free Life ; Immune System/*growth & development/*microbiology ; Immunity, Innate/genetics/*immunology ; Immunity, Maternally-Acquired/genetics/*immunology ; Intestines/*immunology ; Lymphocytes/immunology ; Mice ; Mice, Inbred C57BL ; Pregnancy ; Symbiosis ; Transcription, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 6
    Publication Date: 2015-03-17
    Description: Author(s): A. Deltuva and P. U. Sauer The three-nucleon bound and scattering equations are solved in momentum space for a coupled-channels Hamiltonian. The Hamiltonian couples the purely nucleonic sector of Hilbert space with a sector in which one nucleon is excited to a Δ isobar. The interaction consists of irreducible two-baryon and i... [Phys. Rev. C 91, 034002] Published Mon Mar 16, 2015
    Keywords: Nucleon-Nucleon Interaction, Few-Body Systems
    Print ISSN: 0556-2813
    Electronic ISSN: 1089-490X
    Topics: Physics
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  • 7
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    Publication Date: 2016-06-01
    Description: Motivation: Simple forms of mutualism between microorganisms are widespread in nature. Nevertheless, the role played by the environmental nutrient composition in mediating cross-feeding in microbial ecosystems is still poorly understood. Results: Here, we use mixed-integer bilevel linear programming to investigate the cost of sharing metabolic resources in microbial communities. The algorithm infers an optimal combination of nutrients that can selectively sustain synergistic growth for a pair of species and guarantees minimum cost of cross-fed metabolites. To test model-based predictions, we selected a pair of Escherichia coli single gene knockouts auxotrophic, respectively, for arginine and leucine: argB and leuB and we experimentally verified that model-predicted medium composition significantly favors mutualism. Moreover, mass spectrometry profiling of exchanged metabolites confirmed the predicted cross-fed metabolites, supporting our constraint based modeling approach as a promising tool for engineering microbial consortia. Availability and implementation: The software is freely available as a matlab script in the Supplementary materials. Contact: zampieri@imsb.biol.ethz.ch . Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 10
    Publication Date: 2014-01-16
    Description: Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index . Contact: mikael.sunnaker@bsse.ethz.ch ; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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