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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
  • Metabolism

Metabolism#

CORNETO can be used to infer and contextualize metabolic networks. In this tutorial, we will show how to use CORNETO to infer a metabolic network from a gene expression dataset and a metabolic model. We will then show how to contextualize the inferred network with prior knowledge.

  • Flux Balance Analysis (FBA)
    • What is FBA?
    • FBA with CORNETO
    • Using COBRApy
    • Using CORNETO
    • Sparse FBA
  • Multi-condition sparse FBA
  • Context-specific models (iMAT)
  • Multi-condition iMAT

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Multi-sample PCST

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Flux Balance Analysis (FBA)

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