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DTSTAMP:20260618T122705Z
UID:62bf8837-f73f-4a75-b9b4-51f67ed907f1
DTSTART:20230418T090000Z
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DESCRIPTION:**Event registration is now full.**\n\nCellular signalling netw
 orks are the communication pathways that govern the behaviour of cells. Th
 ey allow cells to receive and process external signals\, such as growth fa
 ctors and hormones\, and respond appropriately by activating specific gene
  expression programs or inducing cellular behaviours like proliferation\, 
 migration\, and differentiation. Disruptions in these signalling networks 
 can lead to various diseases\, including cancer\, metabolic disorders\, an
 d immune disorders. Understanding the regulatory mechanisms of signalling 
 networks is critical to developing effective therapies for these diseases.
 \n\nOne approach to discover signalling network alterations from omics dat
 a is through the use of upstream regulatory pathway analysis\, which aims 
 to identify the transcription factors and upstream signalling regulators t
 hat control the expression of downstream genes. This can be achieved throu
 gh the joint analysis of omics data and the signalling network structure u
 sing methods such as CARNIVAL. By using powerful algorithms and general pu
 rpose integer optimization solvers\, CARNIVAL explores the vast space of p
 otential signalling alterations to identify a parsimonious signalling netw
 ork that explains the measurements.\n\nIn this course\, participants will 
 learn how to process differential gene expression data to estimate transcr
 iption factor activities with [DecoupleR](https://saezlab.github.io/decou
 pleR/)\, obtain and process prior knowledge networks with [OmniPath](http
 s://omnipathdb.org/) and [pypath](https://github.com/saezlab/pypath)\, a
 nd use [CARNIVAL](https://saezlab.github.io/CARNIVAL/) to infer signalli
 ng networks.\n\n 
SUMMARY:From transcriptomics to mechanistic models of signalling
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/transcriptomics-mechani
 stic-models-signalling
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