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CellNOpt specifications

Information


Unique identifier OMICS_06007
Name CellNOpt
Alternative name CellNetOptimizer
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A prior knowledge network (PKN) describing signed and directed interactions between proteins as a graph, biochemical data relating to the changes in the modification state (typically phosphorylation) of proteins following stimulation under various conditions
Input format SIF, MIDAS
Output data The summary of the analysis, hyperlinked to diagnostic graphs, and the trained networks
Output format HTML
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB, Python, R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.20.0
Stability Stable
Requirements
GraphViz, RBGL, graph, methods, hash, ggplot, RCurl, Rgraphviz, XML
Maintained No

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Documentation


Publication for CellNetOptimizer

CellNOpt citations

 (26)
library_books

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines

2017
Cell Syst
PMCID: 5747350
PMID: 29226804
DOI: 10.1016/j.cels.2017.11.002

[…] ed us to generate dynamic mechanistic models based on a qualitative understanding of the processes, but without having a detailed chemical knowledge about all the underlying mechanisms (). Within the CellNetOptimizer (CellNOpt) framework, the same prior-knowledge network was trained with the experimental data from different cell lines to obtain cell-line-specific models (). Because of the requirem […]

library_books

A parallel metaheuristic for large mixed integer dynamic optimization problems, with applications in computational biology

2017
PLoS One
PMCID: 5557587
PMID: 28813442
DOI: 10.1371/journal.pone.0182186

[…] t consists of phosphorylation measurements from a hepatocellular carcinoma cell line (HepG2) at 0, 30 and 180 minutes after perturbation.To preprocess the network, we used CellNOptR, the R version of CellNOpt []. Basically, the network was compressed to remove as many non-observable/non-controllable species as possible. Subsequently, we generated all gates that were compatible with the network; fo […]

library_books

Logic Modeling in Quantitative Systems Pharmacology

2017
PMCID: 5572374
PMID: 28681552
DOI: 10.1002/psp4.12225

[…] (PKN). Here, public databases and resources like Omnipath help to gather and relate known information. When appropriate experimental data are available, they can be used to refine the PKN. Tools like CellNOpt help us in this process. Finally, with a functional model, using tools like MaBoSS, we can simulate different cellular and experimental conditions and predict the effect of pharmacological in […]

library_books

Understanding the mTOR signaling pathway via mathematical modeling

2017
Wiley Interdiscip Rev Syst Biol Med
PMCID: 5573916
PMID: 28186392
DOI: 10.1002/wsbm.1379

[…] friendly tools such as CellDesigner, COPASI, and PyBioS, all of which include methods for parameter estimation and model analysis. Boolean or logic‐based models can be created and simulated by, e.g., CellNetOptimizer, a software suite which allows to train a set of possible logical models against experimental data. […]

library_books

Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

2016
PMCID: 5080650
PMID: 27567007
DOI: 10.1002/psp4.12104

[…] ). We constructed the PKN (Supplementary Figure S1) based on the one from Morris et al. but extended it to include information describing phosphorylation of different protein domains. We modified the CellNOpt training process to add a PKN processing step that added nodes and interactions to allow for the algorithm to capture the effects of inhibition on the basal signal values (Supplementary Figur […]

library_books

Modelling with ANIMO: between fuzzy logic and differential equations

2016
BMC Syst Biol
PMCID: 4962523
PMID: 27460034
DOI: 10.1186/s12918-016-0286-z

[…] al methods, as we concentrate on tools that allow to define the dynamics of biological networks from a more mechanistic point of view. Among related work, we would like to highlight the powerful tool CellNOpt []. CellNOpt is a software which can work with logic descriptions (Boolean, fuzzy) and differential equations, and automatically suggests the best network topologies to match a given data set […]

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CellNOpt institution(s)
European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK; Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA
CellNOpt funding source(s)
This work was supported by the Institute for Collaborative Biotechnologies (contract no. W911NF-09-D-0001 from the U.S. Army Research Office), EU-7FP-BioPreDyn and the EMBL EIPOD program.

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