From omics data to networks to mechanistic models

Trainer: Laurence Calzone and Marco Ruscone 

Overview: This session is divided into two practical parts. In the first part, participants will learn to extract interaction data from databases (e.g., SIGNOR, Omnipath) and apply network enrichment methods to build regulatory networks using the Python package NeKo. In the second part, participants will use the MaBoSS software to simulate Boolean models and interpret biological network behaviours. Practical examples, such as a cell cycle model, will illustrate how genetic mutations and varying initial conditions have an impact on the network dynamics.

Learning outcomes:

By the end of the session, participants will be able to:

• Extract and construct regulatory networks from interaction databases using NeKo.
• Build and simulate Boolean models using MaBoSS.
• Analyze the effects of genetic perturbations on cellular phenotypes.

Materials: folder with materials