Assays

440 Assays visible to you, out of a total of 770

Proteomics data for N15 incorporation into protein in Ostreococcus grown in 12L:12D light:dark cycles.

Contributor: Daniel Seaton

Assay type: Proteomics

Technology type: Mass Spectrometry

Snapshots: No snapshots

Test by Martin for simileXML

Originally submitted to PLaSMo on 2012-03-08 11:39:23

Contributor: BioData SynthSys

Biological problem addressed: Gene Regulatory Network

Snapshots: No snapshots

Test by Martin for simileXML

Version Comments


Version 2, the product of many seconds of research..




Originally submitted to PLaSMo on 2012-03-08 11:39:23

Contributor: BioData SynthSys

Biological problem addressed: Gene Regulatory Network

Snapshots: No snapshots

No description specified

Contributor: Dawie Van Niekerk

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

L. lactis, E. faecalis and S. pyogenes are referred to as LAB because of the fact that in the presence of glucose lactate is produced as the main fermentation product . This metabolic pathway is relatively inefficient, since only 2 ATP are generated from one glucose molecule. All three LAB possess the genetic make up for mixed acid fermentation, a more effective way of fermentation generating 3 ATP per molecule of glucose. All three genomes reveal (at least) two genes encoding a lactate dehydrogenase
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This is a very simple generic vegetation model, with just one state variable (plant biomass), and two processes: assimilation and respiration.   In the original paper, the model is used twice, once for the trees and once for the grass under the trees, with the grass receiving light not intercepted by the trees.   The model provided here is just for a single vegetation component.

Related Publications
McMurtrie RE, Wolf L (1983). A model of competition
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This is a very simple generic vegetation model, with just one state variable (plant biomass), and two processes: assimilation and respiration.   In the original paper, the model is used twice, once for the trees and once for the grass under the trees, with the grass receiving light not intercepted by the trees.   The model provided here is just for a single vegetation component.

Related Publications
McMurtrie RE, Wolf L (1983). A model of competition
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Contributor: BioData SynthSys

Biological problem addressed: Gene Regulatory Network

Snapshots: No snapshots

This .csv file shows the numbers of different cytochrome molecules per cell from steady-state continuously-grown cultures at various aerobiosis levels (0%, 31%, 56%, 85% and 115% AAU).

Contributor: Alison Graham

Assay type: Experimental Assay Type

Technology type: Technology Type

Snapshots: No snapshots

To estimate the relative level of plasma-membrane potential in the wild type, single trk1 and trk2 or double trk1 trk2 mutants, the diSC3(3) fluorescent probe was added to cells grown under various conditions and the levels of cell fluorescence were monitored.

Contributor: Falko Krause

Assay type: Experimental Assay Type

Technology type: Technology Type

Snapshots: No snapshots

To estimate the relative level of plasma-membrane potential in the wild type, single trk1 and trk2 or double trk1 trk2 mutants, the diSC3(3) fluorescent probe was added to cells grown under various conditions and the levels of cell fluorescence were monitored.

Contributor: Falko Krause

Assay type: Experimental Assay Type

Technology type: Technology Type

Snapshots: No snapshots

Genome scale metabolic model of Sulfolobus solfataricus
specific scenario: modelling of L-fucose degradation pathways

Contributor: Jacqueline Wolf

Biological problem addressed: Metabolic Network

Snapshots: No snapshots

No description specified

Contributor: Jay Moore

Biological problem addressed: Metabolic Network

Snapshots: No snapshots

Concentration of glycolytic intermediates over time

Metabolite profiling on T. brucei exposed to methylene blue has been carried out using LC-MS to investigate metabolic changes caused by oxidative stress

Measurement of intra- and extra-cellular metabolome.

Contributor: Ulf Liebal

Assay type: Metabolite Profiling

Technology type: Liquid Chromatography-tandom Mass Spectrometry

Snapshots: No snapshots

No description specified

This Excel template is the general (master) template for any type of metabolomics data. It can be used as it is, or extended and modified to create a more specific templates for particular technologies and assay types.

Contributor: Katy Wolstencroft

Assay type: Metabolomics

Technology type: Technology Type

Snapshots: No snapshots

Validation: Validated against the original running in Excel. Each calculation in the model was individually validated as well. Comments on numerical integration: Euler integration with time steps of 1. In Simile the "time units" were set to "day" and execution was for 364 days as the simulation starts at time 0 (not time 1 as in the Excel model). Comments on running Simile model: Users must specify the temperature controlled growing season themselves. To do this use the following steps which take
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Contributor: Dawie Van Niekerk

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Contributor: Dawie Van Niekerk

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

Lumped kinetic model of L. lactis glycolysis, formulated with ordinary differential equations. Simulations are in line with experimental data

Contributor: Mark Musters

Assay type: Experimental Assay Type

Technology type: Technology Type

Snapshots: No snapshots

Model prediction of the conversion of 3PG to fructose-6-phosphate and the gluconeogenic pathway intermediates.
https://jjj.bio.vu.nl/models/experiments/kouril3_experiment-user/simulate

Model files accompanying Seaton et al., Molecular Systems Biology, 2015 Abstract: Clock?regulated pathways coordinate the response of many developmental processes to changes in photoperiod and temperature. We model two of the best?understood clock output pathways in Arabidopsis, which control key regulators of flowering and elongation growth. In flowering, the model predicted regulatory links from the clock to CYCLING DOF FACTOR 1 (CDF1) and FLAVIN?BINDING, KELCH REPEAT, F?BOX 1 (FKF1) transcription.
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Contributor: BioData SynthSys

Biological problem addressed: Gene Regulatory Network

Snapshots: No snapshots

Modelling the degradation of BPG, GAP and DHAP at high temperature

Based on a kinetic model a description of the potassium current is achieved. Its properties with respect to changes in membrane potential and potassium concentrations are derived.

Contributor: Falko Krause

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

The RNAseq data on mRNA processing and mRNA decay were used to update a previously published model and to interrogate which process should be dependent on mRNA length

Contributor: Jurgen Haanstra

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

This is a modified version of Biomodels89, containing a light-forcing function. This variant is configured to run cycles of LD8:16

Related Publications
ocke JC, Kozma-Bognár L, Gould PD, Fehér B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ. (2006). Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana. . Mol Syst Biol .

Originally submitted to PLaSMo on 2012-03-29 10:24:44
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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.

This assay is designed to measure the decay kinetics of mRNA in T. brucei blood forms. T. brucei lacks the canonical transcriptional regulation employed by other eukaryotes through transcription factors, and relies almost entirely on regulation of mRNA decay and further downstream steps in order to control gene expression.
3 replicates of 8 time points were taken to measure mRNA abundance in the cell using RNA-seq. The first time point was fot WT, untreated cells; the second was 5 min after the
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