Biological problem addressed 'Model Analysis Type'
Model Analysis Type
Cell Cycle (1) | Enzymology (5) | Gene Expression (5) | Gene Regulatory Network (1) | Genome Scale (0) | Metabolic Network (11) | Metabolic Redesign (0) | Metabolism (2) | Post Translational Modification (0) | Signal Induction (1) | Stress Response (0) | Translation (0) | Stress response/Adaptation (0) | Analysis of HMM hits (0) | Multiple sequence alignment (0)
Related assays96 Assays visible to you, out of a total of 160
The main input is the ENA review paper (Function and Regulation of the Saccharomyces cerevisiae ENA Sodium ATPase System, Ruiz&Ariño 2007) and the papers referenced.
Another source are the papers linked from the ENA page of SGD http://www.yeastgenome.org/cgi-bin/locus.fpl?locus=ENA1
A boolean network was created using booleannet (after experimenting with Squad and CellNetAnalyzer). This network can be simulated and visualized using additional software components that will be part of the pyMantis CMS that is developed by the Translucent project.
A interaction network analysis tool (currently based on the BioGrid - PSICQUIC web services) was created that helps to discover interactions of Yeast proteins. The tool will at some point be freely available on the www as part of the pyMantis CMS created within the Translucent project.
Proton fluxes ensue a change in the membrane potential to which the potassium uptake responds. The membrane potential changes depend on the extrusion of protons, buffering capacities of the media and experimental parametes.
Theoretical analysis of hypothetical sigma factor competition.
Based on the model 'transcription factor competition' possible dynamics of sigma factor competition are simulated and analysed using Lineweaver-Burk representations.
We use BSA115 strain which lacks RsbU and RsbW proteins. Therefore, there is limited post-transcriptional regulation of sigmaB activity.
There occurs an unexpected drop in the beta-Gal activity after sigB induction. This modelling effort aims to clarify the reasons.
The dynamic model describes response of yeast metabolic network on metabolic perturbation (i.e. glucose-pulse). One compartmental ODE-based model of yeast anaerobic metabolism includes: glycolysis, pentose phosphate reactions, purine de novo synthesis pathway, purine salvage reactions, redox reactions and biomass growth. The model describes metabolic perturbation of steady state growing cells in chemostat.
- 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
Using PCA, three components, beam size 8.
Clustering via MCL from Biolayout Express 3D
Data is taken from "Genome-Wide Gene Expression Analysis of the Switch between Acidogenesis and Solventogenesis in Continuous Cultures of Clostridium acetobutylicum." Grimmler et al. 2011
Pyruvate formate-lyase (PFL) is an important enzyme in the metabolic pathway of lactic acid bacteria (LAB) and is held responsible for the regulation of the shift between homolactic acid to mixed acid fermentation. PFL catalysis the reversible reaction of acetyl-CoA and formate into pyruvate and CoA. A glycyl radical, who is regenerated within the reaction, is involved; therefore, PFL works only under strictly anaerobic conditions. For its activation, the C-terminal domain has to bind to the
Metabolic network of S. pyogenes including primary metabolism, polysaccharide metabolism, purine and pyrimidine biosoynthesis, teichoic acid biosynthesis, fatty acid and phospholipid bioynthesis, amino acid metabolism, vitamins and cofactors
Metabolic network of Enterococcus faecalis including primary metabolism, polysaccharide metabolism, purine and pyrimidine biosoynthesis, teichoic acid biosynthesis, fatty acid and phospholipid bioynthesis, amino acid metabolism, vitamins and cofactors
Model prediction of the conversion of 3PG to fructose-6-phosphate and the gluconeogenic pathway intermediates.
The stressosome is an important sensor of environmental stresses in B. subtilis. It is formed by three protein types that form an icosahedral geometric protein complex. There are uncertanties how protein interactions take place, what the effects on the response behaviour of activation and inhibition of phosphorylation among proteins is, and what kind of proximal signal activates the stressosome in the first place.
To answer these questions a computational modelling approach was developed. This
Despite high similarity in sequence and catalytic properties, the L-lactate dehydrogenases (LDH) in lactic acid bacteria (LAB) display differences in their regulation which may arise from their adaptation to different habitats. We combined experimental and computational approaches to investigate the effects of fructose-1,6-bisphosphate (FBP), phosphate (Pi) and ionic strength (NaCl concentration) on 6 LDHs from 4 LABs studied at pH 6 and pH 7. We find: (1) The extent of activation by FBP (Kact)
SED-ML simulation: https://jjj.bio.vu.nl/models/experiments/penkler2aa_experiment-user/simulate
RobOKoD algorithm was, designed then implemented as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain. It was shown to perform better than similar methods (OptKnock, and RobustKnock).
OptKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.
RobustKnock algorithm was used as part of a study in RobOKoD: microbial strain design for (over)production of target compounds. (http://fairdomhub.org/publications/236). It was used to generate a strain of e.coli for producing butanol, that was then compared to an experimental strain.
The multi-compartmental metabolic network of Arabidopsis thaliana was reconstructed and optimized in order to explain growth stoichiometry of the plant both in light and in dark conditions. Balances and turnover of energy (ATP/ADP) and redox (NAD(P)H/NAD(P)) metabolites as well as proton in different compartments were estimated. The model showed that in light conditions, the plastid ATP balance depended on the relationship between fluxes through photorespiration and photosynthesis including both
Simulation of double mutants and perturbations and time series samples using for Sample 1 only OE mutants of which we update the enzyme concentrations. For each second mutant the enzyme concentrations in case of OE and KO mutants in updated and the metabolite concentrations of the second sample are loaded in the model.
Using this approach the model approximately predicts combinatorial effects of OE mutations with other mutations, perturbations and time series concentrations.
We have developed a method for comparative analysis of pairs of complex networks based on gene co-expression analysis. We apply this modeling analysis to data set for gene expressions in multiple tissues of mus musculus and homo sapiens.
These Python scripts define and simulate the translational coincidence model. This model takes measured transcript dynamics (Blasing et al, 2005) in 12L:12D, measured synthesis rates of protein in light compared to dark (Pal et al, 2013), and outputs predicted changes in protein abundance between short (6h) and long (18h) photoperiods. These are compared to the photoperiod proteomics dataset we generated.
Data and Python scripts to run the analysis of literature data that estimates rates of protein synthesis in the light and dark, and overall rates of protein turnover, in Cyanothece and Ostrecoccus tauri.