CellNOpt (from CellNetOptimizer; a.k.a. CNO) is a software used for creating logic-based models of signal transduction networks using different logic formalisms (Boolean, probabilistic logic, or differential equations). CellNOpt uses information on signaling pathways encoded as a Prior Knowledge Network, and trains it against high-throughput biochemical data to create cell-specific models.
ShinyCNOR is still under development, and the following approaches are implemented or in the process of being implemented.
CellNOptR for Boolean formalism. It contains the core functions as well as the boolean and steady states version. It implements the workflow described in Saez-Rodriguez et al Mol Sys Bio 2009, with extended capabilities for multiple time points.
CNORprob for Probablistic Logic formalism. It is a probabilistic logic variant of CellNOpt which allows for quantitative optimisation of logical network for (quasi-)steady-state data. The core optimisation pipeline is derived from FALCON: a toolbox for the fast contextualization of logical networks.
CNORode for logic-based ODE formalism. It is an ODE add-on to CellNOptR. It is based on the method of (Wittmann et al BMC Sys Bio 2009), also implemented in the tool Odefy (Krusiek et al BMC Bioinf 2010).