ArticleBMC systems biology2012
CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.
Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
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108 citing papers in PubMed.
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- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- Logic-based modeling of inflammatory macrophage cross talk with glomerular endothelial cells in diabetic kidney disease.American journal of physiology. Renal physiology · 2025Article
- Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025Article
- Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.Genes · 2024Article
- From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling.iScience · 2024Review
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- Article
- Ten quick tips for fuzzy logic modeling of biomedical systems.PLoS computational biology · 2023Article
- Catalyst: Fast and flexible modeling of reaction networks.PLoS computational biology · 2023Article
- kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.BMC bioinformatics · 2023Article
- Reducing Boolean networks with backward equivalence.BMC bioinformatics · 2023Article
- Boolean network sketches: a unifying framework for logical model inference.Bioinformatics (Oxford, England) · 2023Article
- Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses.Frontiers in bioinformatics · 2023Article
- Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search.Frontiers in cell and developmental biology · 2023Article
- An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis.BMC biology · 2022Article
48 more citing papers are in PubMed but not listed here.
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9 authors.
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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.
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