Models

From published files, Uriel Urquiza created SBML models with all 8 parameter sets published, and versions of F2014.1 to simulate multiple clock mutants, using SloppyCell

F2014.1.2 SBML file including Stepfunction imported back into Copasi v4.8

F2014.1.1 becomes the published version, with SBML file originally created from SloppyCell by Uriel Urquiza - see separate file. then Andrew Millar converted into SBML L2V4 in Copasi and added ISSF for light input, using SBSI Stepfunction editor (see Adams et al. 2011 J Biol Rhythms).

Simplified model file for PLaSMo accession ID PLM_71, version 2 (use simplified if your software cannot read the file, e.g. Sloppy Cell)

Originally submitted model file for PLaSMo accession ID PLM_71, version 2

Simplified model file for PLaSMo accession ID PLM_71, version 1 (use simplified if your software cannot read the file, e.g. Sloppy Cell)

Originally submitted model file for PLaSMo accession ID PLM_71, version 1

Originally submitted model file for PLaSMo accession ID PLM_1041, version 1

Simplified model file for PLaSMo accession ID PLM_1041, version 1 (use simplified if your software cannot read the file, e.g. Sloppy Cell)

Arabidopsis clock model P2011.6.1 SBML imported into Copasi 4.8 and saved as native Copasi file.

The P2011.3.1 SBML model imported into Copasi v4.8, saved as native Copasi file

This is BIOMD0000000005.

Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material: TNF and MMP7 concentration upgrade of the models All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF ...

Copasi file chronic inflammation Abulikemu et al 2020 (altered units TNF and MMP8); see supplemntal material: TNF and MMP7 concentration upgrade of the models All computations for the present paper were completed by using the model prepared and tested in Abulikemu et al 2018. Then, little attention was paid to the unit in which concentrations were expressed, except for the concentration of fibroblasts, which we found important for modelling the effect of confluency. This led to a predicted TNF ...

This is a model about a ROS network that exhibits five design principles, and has been calibrated so as to predict quantitatively various steady state concentrations. 10191125.

Instructions RUN the model for steady state. For the Menadione experiment set the initial concentration of 'Menadione' species to experimental dosing i.e. 100 000 nM (0.1 mM) and make the simulation type "reaction" for both the species i.e. 'Menadione' and 'Menadione_internal'. Then run for 24 hr i.e. 1500 minutes approx. ...

Dynamic model of glycolysis, pyruvate metabolism and NoxE. The model is parameterized by selecting the best out of 100 parameter set using Copasi's Genetic algorithm with 1000 itterations and 500 simmulatanious models.

Model that can be used to obtain the figures of Abudulikemu et al 2018: Abudukelimu, A., Barberis, M., Redegeld, F.A., Sahin, N., and Westerhoff, H.V. (2018). Predictable Irreversible Switching Between Acute and Chronic Inflammation. Front Immunol 9, 1596.

(Abudulikemu et al 2000 (also 2018) Standard model of acute mode Figure 32.

Particularly figure 2 of of Abudulikemu et al 2020 in press

desktop version of NetLogo model of cellular senescence in dermal layer of skin

Originally submitted model file for PLaSMo accession ID PLM_73, version 1

Originally submitted model file for PLaSMo accession ID PLM_75, version 1

Originally submitted model file for PLaSMo accession ID PLM_74, version 1

Originally submitted model file for PLaSMo accession ID PLM_76, version 1

The consensus GEM for Saccharomyces cerevisiae, version 8.3.3, maintained on https://github.com/SysBioChalmers/yeast-GEM.

BPG stability notebook

PGK model for S. solfataricus

A small model representing the core carbon network in each cell. For more detail on the model creation see [1]. The model is written in SBML using the RAM extension for use in deFBA. Compatible python software for simulation can be found at https://tinyurl.com/yy8xu4v7

[1] S. Waldherr, D. A. Oyarzún, A. Bockmayr. Dynamic optimization of metabolic networks coupled with gene expression. In: Journal of Theoretical Biology, 365(0): 469 - 485.

This model was created to showcase all functions of the SBML extension RAM. The model can be unr in deFBA with the python software deFBA-Python. The software is freely available at at https://tinyurl.com/yy8xu4v7