Logic modelling of signalling networks – CellNOpt and CARNIVAL

Reconstruction of signalling networks has been widely utilised in the past, for example to understand aberrations in diseased cells, or to figure out mechanism of drug actions. With the development of high throughput data platforms, it is possible to infer these networks from the data alone, alternatively we could reuse existing knowledge about possible mechanisms reported in literature and interaction databases. The prior knowledge network (PKN) describes the possible interactions among the signalling molecules and connects the perturbations to the measured molecular markers. Different formalisms build different types of models from the PKN, ranging from Boolean networks to differential equations. It is then possible to train the models to the measured data using optimisation methods. CellNOpt uses different logic formalisms, which include boolean, fuzzy, probabilistic, and ordinary differential equations models which are trained against (phosphoproteomic) data. On the other hand, similar approaches are used to extract mechanistic insights from multi-omics data using CARNIVAL to train signalling networks from gene expression data using integer linear programming to infer causal paths linking signalling drives with downstream transcripts’ levels.

This webinar is part of PerMedCoE webinar series and is open for anyone interested in simulation of metabolic models, and in PerMedCoE tools and activities. The goal of PerMedCoE is to provide an efficient and sustainable entry point to the HPC/Exascale-upgraded methodology to translate omics analyses into actionable models of cellular functions of medical relevance. No prior knowledge is required.

Outcomes

By the end of this webinar, you will be able to:

• Describe how CellNOpt and CARNIVAL build models of signalling networks