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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
  • Network methods

Network methods#

  • Shortest paths
    • Creating a weighted graph
    • Random graphs
  • Multi-sample shortest paths
  • Multi-commodity Network Flows
  • Acyclic Flows
    • Largest DAG from a given source and sink vertex
    • Largest DAG from any source to any sink
  • Steiner trees
    • What are Steiner Trees?
    • How CORNETO Solves Steiner Tree Problems
      • Key Features:
    • Importing data to CORNETO
    • Solving the problem with the exact steiner tree
    • Extracting the tree and plotting
    • Directed graphs
    • Mixed graphs
  • Prize-Collecting Steiner Trees (PCST)
    • Directed
    • Select a specific root vertex
  • Multi-sample PCST
    • Using CORNETO
    • Multi-sample PCST
    • Testing the effect of \(\lambda\)

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Shortest paths

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