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  • Install
  • Guide
  • Tutorials
  • Reference
  • GitHub

Section Navigation

  • Getting started
    • Prior Knowledge and Graphs
    • Working with data
    • Plotting
    • Constrained Optimization
  • Network methods
    • Shortest paths
    • Multi-sample shortest paths
    • Multi-commodity Network Flows
    • Acyclic Flows
    • Steiner trees
    • Prize-Collecting Steiner Trees (PCST)
    • Multi-sample PCST
  • Metabolism
    • Flux Balance Analysis (FBA)
    • Multi-condition sparse FBA
    • Context-specific models (iMAT)
    • Multi-condition iMAT
  • Signaling
    • CARNIVAL
    • Multi-sample CARNIVAL
    • Acyclic boolean models of cell signaling (experimental)
  • Interoperability
    • COBRApy: Constraint-based metabolic modeling in Python
    • LIANA+: An all-in-one cell-cell communication framework
    • Decoupler: Ensemble of methods to infer biological activities
    • Omnipath: intra- & intercellular signaling knowledge
    • NetworkX: Network Analysis in Python
    • CVXPY: Convex optimization, for everyone
    • PICOS: A Python interface to conic optimization solvers
  • Guide
  • Signaling

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
    • Single sample using the same multi-sample method
  • Acyclic boolean models of cell signaling (experimental)
  • Introduction to a Boolean model
    • Inhibition: !EGF activates RAS
    • Inhibition with AND gates
  • Complex case study with inhibition

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Multi-condition iMAT

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CARNIVAL

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