PerMedCoE webinars are open to everyone interested in PerMedCoE tools and activities. The webinars will include a 30-40 minutes presentation and a Q&A section of around 15 minutes. The recording of this webinar will be publicly available on this web page and the PerMedCoE YouTube channel.
Logic modelling of signalling networks – CellNOpt and CARNIVAL
Speaker: Bartosz Bartmanski (University Hospital Heidelberg – Institute for Computational Biomedicine)
Date and time: Thursday 20 January 2022, 15.00-16.00 CET
Registration: Registration form is available here.
Target group: Anyone interested in PerMedCoE’s tools and activities
Learning outcomes: Overview of the CellNOpt and CARNIVAL applications
Reconstruction of signaling 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 signaling 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 signaling networks from gene expression data using integer linear programming to infer causal paths linking signaling drives with downstream transcripts’ levels. In this webinar, we introduce CellNOpt and CARNIVAL and show how each can be used to build models of signalling networks.
Dr Bartosz Bartmanski is research software engineer at Julio Saez-Rodriguez’s group at the Joint Research-Center for Computational Biomedicine. Prior to this, Dr Bartmanski was a scientific programmer at the European Molecular Biology Laboratory (EMBL) in the Zimmermann lab. His PhD was at the Mathematical Institute of the University of Oxford on “Efficient methods for simulating stochastic reaction-diffusion models on evolving domains” under the supervision of Prof Ruth Baker. In his previous positions he has developed software for simulation and analysis of stochastic reaction-diffusion systems on evolving domains and developed metabolomic annotation pipelines that utilised machine learning. More recently, his focus has shifted to parallelization of software tools for investigating cell signalling.