A reference for the data is found below. J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction Molecular Systems Biology, 5:331, 2009
Subset of the data used in the reference here below. LG Alexopoulos, J. Saez-Rodriguez, BD Cosgrove, DA Lauffenburger, PK Sorger Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. MCP Molecular & cellular proteomics 2010, 9:9 Citations
ExtLiverPCB contains a large PKN and data set used in the references here below.
Morris, Melody K and Saez-Rodriguez, Julio and Clarke, David C and Sorger,Peter K and Lauffenburger, Douglas a, Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS computational biology, 7, 3, 2011 Citation
M. K. Morris, I. Melas, J. Saez-Rodriguez. Construction of cell type-specific logic models of signaling networks using CellNetOptimizer. to appear in Methods in Molecular Biology:Computational Toxicology, Ed. B. Reisfeld and A. Mayeno, Humana Press.
J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link pr otein signaling networks with functional analysis of mammalian signal transduction Molecular Systems Biology, 5:331, 2009
ExtLiverBMC2012 derives from ExtLiverPCB. The data is the same so no new data file is provided. The PKN model is slightly different (See below).
Terfve, C. and Cokelaer, T. and Henriques D. and MacNamara A. and Gonçalves E. and Morris K.M. and van Iersel M. and Lauffenburger A.D. and Saez-Rodriguez J., CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms, BMC Systems Biology 2012, 6:133, Citation
This model is used in several articles and is used for many tests in CNO. It was part of the DREAM4 challenge. It is also a subset of a larger model, Liver data used in the reference here below.
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. J. Saez-Rodriguez, L.G. Alexopoulos, J. Epperlein, R. Samaga, D.A. Lauffenburger, S. Klamt, P.K. Sorger. Molecular systems Biology, 5, 331, (2009). Citation.
This model comes from the reference here below. The SIF file was generated from the reactions file found on CNA website (Tcell data from 2007 published in Plos Computational biology, Aug 2007 Saez et al.). The generation was performed using read_reactions.py that converts the reaction to SIF file called TCellPCB2007.sif.
Saez-Rodriguez, J., Simeoni, L., Lindquist, et al. A Logical Model Provides Insights into T Cell Receptor Signaling. PLoS Computational Biology, 3(8), 11. Public Library of Science. Citation.
This model/data is the Toy Model published in the reference here below. Model and data were generated by hand following the data found in the reference.
J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link pr otein signaling networks with functional analysis of mammalian signal transduction Molecular Systems Biology, 5:331, 2009.
This directory contains the file PKN-ToyMMB.sif, a model used in several articles and for many tests in CellNOpt. The accompanying data MD-ToyMMB.csv was generated manually by Camille Terfve for the CellNOptR tutorial.
M. K. Morris, I. Melas, J. Saez-Rodriguez. Construction of cell type-specific logic models of signaling networks using CellNetOptimizer. to appear in Methods in Molecular Biology:Computational Toxicology, Ed. B. Reisfeld and A. Mayeno, Humana Press.
C. Terfve. Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR. Bioconductor Link
This model is a variant of ToyMMB with a feedback loop.
This model is a variant of ToyModelMMB with a feedback loop and AND gates.
This model is the same as ToyMMB with an extra link from PI3K to NFkB.
M. K. Morris, I. Melas, J. Saez-Rodriguez. Construction of cell type-specific logic models of signaling networks using CellNetOptimizer. to appear in Methods in Molecular Biology:Computational Toxicology, Ed. B. Reisfeld and A. Mayeno, Humana Press.
This directory contains the example to test the analysis of a 2-time point data set. The file PKN-ToyMMB_T2.sif is a model used in several articles and for many tests in CellNOpt. The accompanying data MD-ToyMMB_T2.csv was generated by Camille Terfve for the CellNOptR tutorial.
In addition to PKN-ToyMMB.sif, this model contains a inhibitor from cJun to Jnk.
C. Terfve. Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR. Bioconductor Link
The PKN network PKN-ToyPB.sif is used in the reference below. It was generated manually based on the network contained in Saez-Rodriguez et al. (2011). This network is designed to output a variety of dynamics (oscillations, transience etc.) to test all CNOR formalisms.
Note that in the paper cited here below (MacNamara), the data was generated using CNORode software with a modified version of the model. The True Model is called PKN-ToyPB_True.sif. The differences are as follows:
| PKN-ToyPB | PKN-ToyPB_True |
|---|---|
| map3k7–>mkk4 OR map3k7–>mkk4 | This is a logical AND gate only |
| egfr–>sos OR ph–/sos | This is a logical AND gate only (egfr–>sos AND ph–/sos ) |
| connection from pi3k to rac added | |
| connection from tnfr to pi3k added | |
| new node (rac) between pi3k and map3k1 | |
| Node ask1 removed |
MacNamara et al. State-time spectrum of signal transduction logic models Physical Biology, submitted (2012).
Note: The directory contains a SIF file named ToyModelPB.sif but also a PKN-ToyPB_shuffled.sif that has exactly the same topology. However, this second file has a different arrangements of reactions. When compressed and expanded, the resulting models are different. This is a known bug and these 2 files can be used to debug that issue. The file MD-ToyPB_2timePoints.csv is a subset of ToyModelPB.csv that contains the time points 0, 10 and 30 seconds only.
This model is used in the references here below. The network was generated manually from the paper. The data was generated manually from the matlab data in the CNO matlab repository (Data/SampleData/ToyModelPCB). Experiments with stimuli set to zero (all NA) were removed.
Morris, Melody K and Saez-Rodriguez, Julio and Clarke, David C and Sorger, Peter K and Lauffenburger, Douglas a, Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS computational biology, 7, 3, 2011 Citation
The PKN network PKN-ToyPB_SBML.sif is made of the content of the SBMLqual Model.xml used in the reference below. This is almost the same model as PKN-ToyPB_True.sif except for an additional self-loop added on ph node.
There is no data provided but one could use the data used in ToyPB that was generated from ToyPB_True model (same except for the self-loop).
Chaouiya et al. SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC Systems Biology 2013, 7:135
A detailed description of the prior knowledge network and the dataset can be found on the reference below:
P. Traynard, L. Tobalina, F. Eduati, L. Calzone, J. Saez-Rodriguez. Logic Modeling in Quantitative Systems Pharmacology. CPTPharmacometrics Syst. Pharmacol. (2017) 00, 00; doi:10.1002/psp4.12225
Supplementary materials can be found on GitHub. Supplementary files include: model files, codes for CellNOptR and for pypath, results of analysis (logic-based ode parameters, MaBoSS simulations and genetic interactions), and a description of a step-by-step extension using pypath.
Cell-type specific parameters of signalling pathway models as biomarkers for drug sensitivity.
A detailed description of the prior knowledge network and the dataset can be found on the reference below:
F. Eduati, V. Doldan-Martelli, B. Klinger, T. Cokelaer, A. Sieber, F. Kogera, M. Dorel, M. J. Garnett, N. Bluthgen, J. Saez-Rodriguez. Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type–Specific Dynamic Logic Models. Cancer Research DOI: 10.1158/0008-5472.CAN-17-0078 Published June 2017.
Supplementary materials can be found on GitHub link
