13C Metabolic Flux Analysis of Bacillus subtilis 168 in continuous high-salinity culture
- Comparison of metabolic flux distribution in carbon core metabolism (EMP, PPP, TCA) of Bacillus subtilis under 3 different conditions: "salt-free" reference, "stress" chemostat, "osmoprotected" chemostat.
- Model created using OpenFLUX and Microsoft Excel
- Model computed using MatLAB
SEEK ID: https://fairdomhub.org/assays/138
Modelling analysis
Projects: BaCell-SysMO
Investigation: Multiomics study of Bacillus subtilis under osmotic stress
Study: Fluxome analysis of Bacillus subtilis 168 under osmotic stress
Assay position:
Biological problem addressed: Metabolic Network
Organisms: No organisms
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Created: 12th Apr 2011 at 11:32
Last updated: 8th Nov 2017 at 14:21
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Projects: BaCell-SysMO
Institutions: University of Braunschweig
SysMO is a European transnational funding and research initiative on "Systems Biology of Microorganisms".
The goal pursued by SysMO was to record and describe the dynamic molecular processes going on in unicellular microorganisms in a comprehensive way and to present these processes in the form of computerized mathematical models.
Systems biology will raise biomedical and biotechnological research to a new quality level and contribute markedly to progress in understanding. Pooling European research ...
Projects: BaCell-SysMO, COSMIC, SUMO, KOSMOBAC, SysMO-LAB, PSYSMO, SCaRAB, MOSES, TRANSLUCENT, STREAM, SulfoSys, SysMO DB, SysMO Funders, SilicoTryp, Noisy-Strep
Web page: http://sysmo.net/
BaCell-SysMO 2 Modelling carbon core metabolism in Bacillus subtilis – Exploring the contribution of protein complexes in core carbon and nitrogen metabolism.
Bacillus subtilis is a prime model organism for systems biology approaches because it is one of the most advanced models for functional genomics. Furthermore, comprehensive information on cell and molecular biology, physiology and genetics is available and the European Bacillus community (BACELL) has a well-established reputation for applying ...
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Public web page: http://www.sysmo.net/index.php?index=53
High salinity chemostat cultivation, multiomics sampling (proteome, transcriptome, metabolome, fluxome) and modelling of carbon core metabolism of Bacillus subtilis 168.
Submitter: Sandra Maass
Studies: B. subtilis_SysMo2_Chemostat_growthrate-salt, Fluxome analysis of Bacillus subtilis 168 under osmotic stress
Assays: 13C Metabolic Flux Analysis of Bacillus subtilis 168 in continuous high-..., Absolute quantification of proteins by the AQUA-technology, Absolute quantification of proteins using QconCAT technology, Relative quantification of proteins by metabolic labeling, Transcriptome data for chemostat cultivated samples, extracellular metabolites, intracellular metabolites
3 chemostat experiments:
each in 4 biological replicates incl. 1 fed with labelled glucose T = 37°C pH = 7.1 V_R = 300 mL (dasgip parallel bioreactor system) V_G = 9 sL/h (0.5 vvm) M9 Minimal medium + 3,4-dihydroxybenzoate (chelating agent) + 1g/L Glucose n = 1000 rpm
3 conditions:
"reference" without additional sodium chloride as control "stress" supplemented with 1.2M sodium chloride "osmoprotection" supplemented with 1.2M sodium chloride and 1mM glycine betaine (osmoprotectant)
Submitter: Michael Kohlstedt
Investigation: Multiomics study of Bacillus subtilis under osm...
Assays: 13C Metabolic Flux Analysis of Bacillus subtilis 168 in continuous high-...
Excel sheet contains:
- flux distribution solution from best iteration cluster
- quality of the fit (experimental MIDs vs. simulated MIDs)
- Sensitivity analysis for 95% flux parameter confidence interval using a Monte-Carlo approach
Excel sheet contains:
- flux distribution solution from best iteration cluster
- quality of the fit (experimental MIDs vs. simulated MIDs)
- Sensitivity analysis for 95% flux parameter confidence interval using a Monte-Carlo approach
Excel sheet contains:
- flux distribution solution from best iteration cluster
- quality of the fit (experimental MIDs vs. simulated MIDs)
- Sensitivity analysis for 95% flux parameter confidence interval using a Monte-Carlo approach
