Signaling

Inferring signaling networks from omics data is a challenging task due to the complexity of the underlying biological processes. CORNETO provides a set of tools to infer and contextualize signaling networks from omics data and prior knowledge.

• CARNIVAL
  • Creating a toy example
  • CarnivalFlow
  • Carnival ILP
  • Old implementation
• Multi-sample CARNIVAL
  • Toy example
  • Multi-sample CARNIVAL
  • Plotting solutions on top of the PKN
  • Penalizing indirect signaling rules
  • Single sample using the same multi-sample method
• PHONEMeS
  • Choosing the appropriate PHONEMeS workflow
  • Understanding phosphosite scores
  • Computing scores from differential results
  • Inferring a network for one condition
  • Reading and plotting the inferred subnetwork
  • Controlling network size with edge costs
  • Comparing multiple conditions
  • Advanced: implementation in CORNETO
    • Internal solution variables
    • Advanced Data interface
    • Reference-data validation
• Global upstream and downstream PHONEMeS
  • A small runnable example
  • Reading the inferred network
  • Choosing how anchors participate
  • Multiple conditions
  • Why this differs from original upside-down PHONEMeS
  • Scores and edge costs in real analyses
  • Advanced: the general Data interface
  • Advanced: implementation in CORNETO
• Inferring intracellular signaling models with CellNOptDAG
  • One signaling model, several perturbation conditions
  • Relationship to CellNOptR
  • Example: perturbations and two signaling readouts
    • Define the prior-knowledge network
    • Define the perturbation experiment
  • Fit one pathway to the complete experiment
  • Which signaling reactions are supported?
    • Compare the candidate network with the fitted model
    • Inspect the fitted signaling model
  • Does the model reproduce the measured responses?
  • A pathway reaction can be present but inactive
    • An inhibitor changes its target, not the whole cell
  • A compact view for larger signaling experiments
  • Under the hood
    • Endpoint Boolean assumptions
    • Fit and model size
    • What CORNETO adds
    • Inspecting the inferred states
    • Structural connectivity diagnostic
  • Biological interpretation and limitations
  • Connection to causal structure learning