In this section you can find self-learning training materials developed by PerMedCoE technical experts




Training materials on MaBoSS

MaBoSS is a tool for continuous time boolean modelling of biological systems. In addition to the main MaBoSS homepage, web (WebMaBoSS) and Python (pyMaBoSS) extensions are available. In this section you can find tutorials and demos to start using MaBoSS tools.

Tutorials on COBREXA

COBREXA.jl provides tutorials and notebooks with the purpose of explaining the most important concepts and functions for metabolic modelling and model handling to users, and then practicing them.

The documentation contains a quick start guide, examples and tutorials that explain the use of analysis methods to solve common tasks, and a guide for running the analyses on a HPC that describes the functionality required for parallel execution of large analyses

Tutorial on CellNOpt

This tutorial aims to be an introduction to

i) the preparation of the Prior knowledge network (PKN) of signaling pathways and

ii) the training of the PKN against biochemical data to create cell-specific models.

Tutorial on CARNIVAL

This is a tutorial to guide the analysis of RNAseq dataset using footprint based tools such as DOROTHEA, PROGENY and CARNIVAL

Tutorial on how to develop Building Blocks using the ‘permedcoe’ package

This section provides a step-by-step by tutorial on how to develop Building Blocks using the permedcoe package and an application that uses them.

Working with computing clusters: information and resources

This page collects together general information and resources for working with different computing clusters hosted by HPC centres and universities affiliated with PerMedCoE.

Tutorial on working with computing clusters

This tutorial provides an introduction to how to access a computing cluster, manage your data and submit jobs using a batch job system.

Introductory tutorial on using MPI with containers

In this material we will briefly touch upon why MPI is such a tricky component, explain the main approaches to combine MPI with containers and discuss when it is actually worthwhile to use MPI containers.