Biological problem addressed 'Model Analysis Type'

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.

Elucidation of protein networks involved in the regulation of cation homeostasis using protein interaction datasets.

Development of bioinformatic tools to investigate the role of transcription factors and 14-3-3 proteins in the regulation of genes involved in cation homeostasis.

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.

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.

Analytical methods and computational analyses (regression, fitting) will be employed to find properties of the Trk system under different external conditions.

Time-dependent simulations of the dynamic switch between acidogenesis and solventogenesis based on the metabolic network and pH-dependent regulation of the enzymes.

Steady state study of the effect of altering gene regulation on yields of end-products, focusing on butanol.

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 DOI: 10.1159/000320973

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

Using Taverna for mining and MATLAB for conversion into specific formats (cytoscape, SBTOOLBOX2)

Cytoscape based analysis and yED based representation of clostridial Reactomes

The model describes the behaviour of E. coli in a stationary chemostat with different oxygen availability.

Using TFinfer2 to analyse data from "Characterization of MG1655 and mutant strains under conditions of glucose excess and limitation"

Mathematical model for PGK kinetics, saturation with ADP, ATP, 3PG and BPG.

In vitro reconstitution of the PGK, GAPHD, TPI and FBPAase enzymes from S. solfataricus

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

Mathematical model for GAPDH kinetics, saturation with BPG, NADPH, NADP, GAP and Pi

Mathematical model for TPI kinetics, saturation with GAP and DHAP, and inhibition by 3PG and PEP

Mathematical model for FBPAase kinetics, saturation with DHAP and GAP

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

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) ...

We here create a kinetic model for a single enzyme within the T. brucei trypanothione synthesis pathway, the enzyme trypanothione synthetase based on the insights from the laboratory experiments

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

Kinetic characterisation en mathematical modelling of HK.

Kinetic characterisation en mathematical modelling of PGI.

Kinetic characterisation en mathematical modelling of PFK.

Kinetic characterisation en mathematical modelling of ALD.

Kinetic characterisation en mathematical modelling of TPI.

Kinetic characterisation en mathematical modelling of G3PDH.

Kinetic characterisation en mathematical modelling of GAPDH.

Kinetic characterisation en mathematical modelling of PGK.

Kinetic characterisation en mathematical modelling of PGM.

Kinetic characterisation en mathematical modelling of ENO.

Kinetic characterisation en mathematical modelling of PK.

Kinetic characterisation and mathematical modelling of LDH.

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.

Inhibition of lactate flux due to glucose transport inhibitor

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

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 ...

SED-ML simulation: https://jjj.bio.vu.nl/models/experiments/bachmann2011/simulate

SED-ML simulation: https://jjj.bio.vu.nl/models/experiments/bachmann2011/simulate

Growth on D-glucose served as reference scenario

effects of 1% increase in each parameter, more detailed analysis of water content

correlations of starch mobilisation and fresh weight under single parameter changes

Combination of multiple sub-models to form Framework Model version 2

Comparison of simulated wild-type and prr7prr9 double mutant under 12L:12D cycles. Simulation with CVODE simulator via SBSI v1.5 framework.

Simulation data from FMv2 calibrated for experiment L&H2, an experiment run at 18.5C instead of the 20.5C of the replicate and related studies. The Excel file includes the mean and SD of the relevant experimental data, and the figure panels.

Training of the model, parameter estimation using Evolutionary Programming using metabolomics, proteomics and some flux data.

Validation by simulating independent OE, KO mutant and perturbation samples, using sampling of the gausian distribution based on the mean and SD of measurements per sample. A 1000 samples of the gausian distribution of the mean and SD was performed per sample to show error in the measurements and how it propegates in predicted metabolite concentration in SS

Simulation of OE mutants targetting enzymes in the model, combined with metabolite concentrations and enzyme fold change of from the 40 samples. 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.

Construction and manual curated Genome Scale Metabolitic model of M. hyopneumoniae. Dynamic flux balance analysis was performed for glucose uptake

A model for the PGK reaction of yeast in presence or absence of the ATP recycling reactions

BPG stability analysis

PGK 70C model

PGK - GAPDH models

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.

RNA timeseries data for Arabidopsis Col wild-type plants and clock mutants, as separate mean and SD files. The raw data is available on BioDare.ed.ac.uk, and is linked as 'Attribution' from elsewhere on FAIRDOMHub.

The starting models are included here in their original forms, the P2011 model as an SBML L3V1 model file, and the KF2014 model of Fogelmark et al. shared as SBML; both prepared by Uriel Urquiza.

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.

General sandbox

Model of glycolytic oscillations in individual yeast cells in microfluidic flow chamber

In future we should split these versions into separate Assays, and link to the four, original component models, when they are imported with the PlaSMo resource into FairdomHub (expected late 2018)

Contains the analysis of the internal metabolite concentrations of the 40 independend samples Pearson correlation was used to generate heatmaps Pearson correlation with p-value cutof of 0.001 was used and as input for a correlation network (grouping using H-clust) Principal component analysis was performed on samples, F-ion and H-ion data combined and seperately Zip files contains the data (FC.txt), PCA and heatmap plots and the script to re-generate these plots

Metabolic control analysis: Local control coefficients for 40 independent samples based on 100x sampling from the measurement distribution Global control analysis based on 100.000 Latin Hypercube sampling from the parameter search range (0.01-100 for Km values and 0.001-1000 for Vmax values)

The associated zip files contains all input files and a Jupyter notebook to rerun sampled simmulations, combined simmulations, parameter scan for the model with addition of an oxygin inhibiton of LDH, local- and global-sensitivity analysis and plot simmulation output in various formats. In addition the zip file contains the py36.yaml file that can be used to recreate the model simmulation environment using Anaconda making all simmulations completely reproducable. All information on how to use ...

This model is termed P2011 and derives from the article: The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops. Alexandra Pokhilko, Aurora Piñas Fernández, Kieron D Edwards, Megan M Southern, Karen J Halliday & Andrew J Millar Mol. Syst. Biol. 2012; 8: 574, submitted 9 Aug 2011 and published 6 March 2012. Link Link to Supplementary Information, including equations. Minor errors in the published Supplementary Information are described in a file attached ...

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"3PG is an acronym for Physiological Principles Predicting Growth. It is a generalized forest carbon allocation model, published by Landsberg and Waring (1997), that works with any forest biome and can be run as an Excel spreadsheet by practicing foresters given a few days of training. The model uses relatively simple and readily available inputs such as species growth tables, latitude, aspect, weather records, edaphic variables, stand age, and stand density to derive monthly estimates of gross ...

Penman Evaporation over water ( mm/day ). This is a submodel of AFRC Wheat 2 model in Simile notation (the XML version will follow shortly).

Related Publications
Porter J (1993). AFRCWHEAT2: A Model of the Growth and Development of Wheat Incorporating Responses to Water and Nitrogen. . Eur. J. Agron. 2(2): 69-82..

Originally submitted to PLaSMo on 2011-02-04 15:17:42

Number of days between 2 Julian days allowing for change of year and leap years. Assumptions : The gap between the two dates is less than 1 year also JDAY1 is before JDAY2. This is a submodel of AFRC Wheat 2 model in Simile notation (the XML version will follow shortly).  Related PublicationsPorter J (1993). AFRCWHEAT2: A Model of the Growth and Development of Wheat Incorporating Responses to Water and Nitrogen. . Eur. J. Agron. 2(2): 69-82.. Originally submitted to PLaSMo on 2011-02-04 15:24:25 ...

Transform Calendar day to Julian Day. Converts day, month, year into the equivalent Julian Day allowing for leap years. This is a submodel of AFRC Wheat 2 model in Simile notation (the XML version will follow shortly).Related PublicationsPorter J (1993). AFRCWHEAT2: A Model of the Growth and Development of Wheat Incorporating Responses to Water and Nitrogen. . Eur. J. Agron. 2(2): 69-82.. Originally submitted to PLaSMo on 2011-02-04 15:30:45

To calculate leaf and sheath dimensions for main stems and tillers given the emergence length of their leaves and empirical relationships linking leaf number to maximum laminar length. All sizes are in mm. This is a submodel of AFRC Wheat 2 model in Simile notation (the XML version will follow shortly). All variables and parameters that are inputs to the submodel are in the "inputs " submodel box, all variables changed by the submodel are outputted via the "outputs" submodel box.Related ...