CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms

Abstract

BackgroundCells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce.ResultsHere we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.ConclusionsModels generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

Cyrface: An interface from Cytoscape to R that provides a user interface to R packages

“…CytoCopteR 10 provides a simple step-by-step interface allowing users without any experience in R to use the CellNOptR ( www.cellnopt.org) package and handle the input and output networks in Cytoscape . CellNOptR is an open-source software package that provides methods for building predictive logic models from signalling networks using experimental measurements.…”

“…Then, to illustrate the applicability of Cyrface , we show two existing packages, CytoCopteR 10 and DrugVsDisease (DvD) 11 , that make use of Cyrface , and we create a simplified version of the DataRail 12 workflow to process and visualize experimental data using methods available in R . Finally, we discuss on-going and future developments.…”

Logic Modeling in Quantitative Systems Pharmacology

“…The steps include the building of the signaling network, its improvement using available data, and its simulation and analysis geared towards obtaining useful biological insights and predictions. We also present several tools that are useful for such a workflow, namely, Omnipath 48 to construct the signaling network, CellNOpt 49 to build a model trained to data, MaBoSS 50 to simulate and predict treatment response, and Cytoscape 51 to visualize some results of the simulations. It should be noted that many other excellent tools to model and analyze signaling networks exist, such as BoolSim,52 BoolNet,53 GINsim,54 etc.…”

“…In this tutorial we use Omnipath 48 for signaling database mining, CellNOpt 49 for model fitting, MaBoSS 50 for simulations, and Cytoscape 51 for visualization and network analysis. Different steps of the pipeline include 1) selecting a system and a question of interest and building a first version of the network, 2) choosing a modeling formalism and improving the model with data, and 3) analyzing the model, making predictions and comparing them to experimental data.…”

“…Extensions have been developed to overcome this limitation. In this tutorial we will show two of these alternatives with two selected tools: CellNOpt ,49 which implements logic‐based ODEs, and MaBoSS ,50 a tool for continuous time Boolean modeling.…”