CNORdt

CNORdt is an open-source R software package that enables constructing predictive logic models of signaling networks by training networks derived from prior knowledge using signaling data, typically phosphoproteomic data. The package offers various logic formalisms within a unified framework, ranging from Boolean models to differential equations. These different logic model representations can accommodate state and time values with increasing levels of detail.

Key features of CNORdt include:

1. Integrating prior knowledge and context-specific data generates cell line—and context-specific models, resulting in improved predictive and mechanistic insights.

2. Flexibility in model building, allowing users to choose the appropriate logic formalism based on the richness of the available data.

3. An interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape's network-based capabilities.

Topic

Molecular interactions, pathways and networks;Systems biology

Detail

  • Operation: Optimisation and refinement;Modelling and simulation

  • Software interface: Command-line user interface, Library

  • Language: R

  • License: GNU General Public License, version 2

  • Cost: Free

  • Version name: 1.46.0

  • Credit: The Institute for Collaborative Biotechnologies, EU-7FP-BioPreDyn, the EMBL EIPOD program.

  • Input: Pathway or network [sif] [TSV] [Biological pathway or network format]

  • Output: Pathway or network report [sif] [HTML] [Graph format] [PDF]

  • Contact: A. MacNamara aidan.macnamara@ebi.ac.uk

  • Collection: -

  • Maturity: Stable

Publications

  • CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.
  • Terfve C, et al. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. 2012; 6:133. doi: 10.1186/1752-0509-6-133
  • https://doi.org/10.1186/1752-0509-6-133
  • PMID: 23079107
  • PMC: PMC3605281

Download and documentation


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