Models
A model of the circadian regulation of starch turnover, as published in Seaton, Ebenhoeh, Millar, Pokhilko, "Regulatory principles and experimental approaches to the circadian control of starch turnover", J. Roy. Soc. Interface, 2013. This model is referred to as "Model Variant 2". The other model variants are all available from www.plasmo.ed.ac.uk as stated in the publication. Note that the 'P2011' circadian clock model was modified for this publication (as described), in order to replicate the ...
Creators: Andrew Millar, Daniel Seaton
Submitter: Andrew Millar
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Matlab model (could not be represented in SBML) from publication with 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. ...
Creators: Andrew Millar, Daniel Seaton
Submitter: Andrew Millar
Model type: Ordinary differential equations (ODE)
Model format: Matlab package
Environment: Matlab
The Folder contains:
- The MCMC and simulation results, as well as the synthetic data of the Chemical Reaction Network model (DoubleDecayIndep)
- The MCMC and simulation results, as well as the synthetic data of the Lotka-Volterra model (LotkaVolterraJoint)
Together with an executable ipynb script (Exe.ipynb) and the MCMC plotting and execution functions (MCMCFunctions.py).
Creator: Vincent Wagner
Submitter: Vincent Wagner
Model type: Ordinary differential equations (ODE)
Model format: Not specified
Environment: Not specified
Creator: Jana Musilova
Submitter: Jana Musilova
Model type: Metabolic network
Model format: Not specified
Environment: Not specified
A population of turtles have between 1 and 3 genes contributing to the strength of selective destruction, which can either cause ageing or allow for negligible senescence.
Creator: James Wordsworth
Submitter: James Wordsworth
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Model of selective destruction in a single population of cells with differing sensitivities for growth. Fast growing cells can be epigenetically converted to slower cells rather than simple cell death as in previous models.
Creator: James Wordsworth
Submitter: James Wordsworth
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Model of unselective destruction in a single population of cells with differing sensitivities for growth
Creator: James Wordsworth
Submitter: James Wordsworth
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Model of selective destruction in a single population of cells with differing sensitivities for growth
Creators: James Wordsworth, Daryl Shanley, Hannah O'Keefe
Submitter: James Wordsworth
Model type: Agent based modelling
Model format: Not specified
Environment: Not specified
Creator: Vincent Wagner
Submitter: Vincent Wagner
Model type: Not specified
Model format: Not specified
Environment: Not specified
Originally submitted model file for PLaSMo accession ID PLM_1030, version 1
Creators: Andrew Millar, Uriel Urquiza Garcia, BioData SynthSys
Submitter: BioData SynthSys
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model derived from U2019.2, fitted to TiMet data mutants data set. Fixed parameters are scaling factors, COP1 and cP parameters. The rest of the parameters were left optimisable. The networks used in the fitting include WT, lhycca1, prr79, toc1, gi and ztl. The ztl network was only used for fixing the period in this mutant. Then final parameter values for transcription rated were obtained by taking the product of scaling factor and either transcription or translation, the latter required for ...
Creators: Andrew Millar, Uriel Urquiza Garcia
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model derived from U2019.1 in which the transcription rates were rescaled to match the scale of TiMet data set for absolute units of RNA concentration. The gmX scaling parameters in the model were fitted numerically. This model has equivalent dynamics to P2011.1.2.
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model derived from U2020.2, fitted to the TiMet RNA data for wild-type and clock mutants. Fixed parameters are scaling factors, COP1 and cP parameters. The rest of the parameters were left optimisable. The networks used in the fitting include WT, lhycca1, prr79, toc1, gi and ztl. The ztl network was only used for fixing the period in this mutant. Then final parameter values for transcription rates were obtained by taking the product of scaling factor and either transcription or translation, the ...
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model derived from U2020.1 by fitting the scaling factors for matching TiMet data set for wild-type and clock mutants, in absolute units.
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model derived from U2019.1, in which the way the PRR genes are regulated is modified. Repression mechanism introduced Instead of activation between the PRRs for producing the wave of expression. This is inspired in the result of three models P2012, F2014 and F2016. P2012 introduced TOC1 repression in earlier genes relative to its expression. F2014 introduced also the backward repression of PRR9 |-- PRR7 |--- PRR5, TOC1. However little attention was given to why there is a sharper expression ...
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model written in Antimony human-readable language and then translate into SBML using Tellurium
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Copasi
Model written in Antimony human-readable language, Model used in Pokhilko et al 2012
Creators: Uriel Urquiza Garcia, Andrew Millar
Submitter: Uriel Urquiza Garcia
Model type: Ordinary differential equations (ODE)
Model format: Not specified
Environment: Not specified
autogenerated equation listing from the SBML of U2020.3, as a .PDF file
Creators: Andrew Millar, Uriel Urquiza Garcia
Submitter: Andrew Millar
Model type: Ordinary differential equations (ODE)
Model format: PDF (Model description)
Environment: Not specified
autogenerated equation listing from the SBML of U2019.3, as a .PDF file
Creators: Andrew Millar, Uriel Urquiza Garcia
Submitter: Andrew Millar
Model type: Ordinary differential equations (ODE)
Model format: PDF (Model description)
Environment: Not specified
NLRP3 inflammasome activation
Creators: Julia Somers, Gökçe Yağmur Summak, Ebru Kocakaya
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
Thrombotic complications and coagulopathy in COVID-19
Creators: Goar Frischmann, Gisela Fobo, Corinna Montrone
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
Kynurenine synthesis pathway
Creators: Julia Somers, Gökçe Yağmur Summak, Ebru Kocakaya
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
TGF beta signalling
Creator: Francesco Messina
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
The role of the interaction between the SARS-CoV-2 Spike protein and the renin-angiotensin pathway, in particular human ACE2 in pulmonary blood pressure regulation
Creators: Andreas Ruepp, Corinna Montrone, Gisela Fobo, Enrico Glaab
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
The Interferon-lambda (IFNL) map describes the action of the drug candidate IFNL on intra- and intercellular signal transduction under SARS-CoV-2.
Creators: Marius Rameil, Vanessa Nakonecnij, Marta Conti
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
The relation of the interferon 2 pathway and SARS-CoV-2.
Creators: Anna Niarakis, Vidisha Singh, Sara Sadat AGHAMIRI
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
The pathway of heme metabolism under COVID-19, involving Orf3a and Orf9c
Creators: Emek Demir, Julia Somers
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
The impact of SARS-CoV-2 on the apoptosis pathway
Creators: Anna Niarakis, Vidisha Singh, Sara Sadat AGHAMIRI
Submitter: Marek Ostaszewski
Model type: Graphical model
Model format: SBML
Environment: Not specified
Here, we describe the index file generation of the mm10 genome, the genome alignment with kallisto, and quantification with bustools to obtain the used spliced / unspliced transcript input.
Creator: Markus Wolfien
Submitter: Markus Wolfien
Model type: Not specified
Model format: Not specified
Environment: Not specified
Here is the detailed R script to generate the input needed by scSynO for synthetic cell generation and classification model training.
The code that can be embedded into any other Seurat data processing workflow is:
cell_expression_target_cluster
Creator: Markus Wolfien
Submitter: Markus Wolfien
Model type: Not specified
Model format: Not specified
Environment: Not specified