Models

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.

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.

Model of unselective destruction in a single population of cells with differing sensitivities for growth

Model of selective destruction in a single population of cells with differing sensitivities for growth

Originally submitted model file for PLaSMo accession ID PLM_1030, version 1

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 ...

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.

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 ...

Model derived from U2020.1 by fitting the scaling factors for matching TiMet data set for wild-type and clock mutants, in absolute units.

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 ...

Model written in Antimony human-readable language and then translate into SBML using Tellurium

Model written in Antimony human-readable language, Model used in Pokhilko et al 2012

autogenerated equation listing from the SBML of U2020.3, as a .PDF file

autogenerated equation listing from the SBML of U2019.3, as a .PDF file

NLRP3 inflammasome activation

Thrombotic complications and coagulopathy in COVID-19

Kynurenine synthesis pathway

TGF beta signalling

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

The Interferon-lambda (IFNL) map describes the action of the drug candidate IFNL on intra- and intercellular signal transduction under SARS-CoV-2.

The relation of the interferon 2 pathway and SARS-CoV-2.

The pathway of heme metabolism under COVID-19, involving Orf3a and Orf9c

The impact of SARS-CoV-2 on the apoptosis pathway

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.

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:

cellexpressiontarget_cluster

Single nuclei transcriptomics data as .csv files from the Allen Brain atlas data set of mus musculus (https://celltypes.brain-map.org/) have been utilized as an input for scSynO. The underlying analysis is part of the manuscript entitled "Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling". Data anaylsis and visalizations were mainly generated with the Seurat R package (https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html)