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

Camille Terfve1, Thomas Cokelaer1, David Henriques1, Aidan MacNamara1, Emanuel Goncalves1, Melody K Morris2, Martijn van Iersel1, Douglas A Lauffenburger2 and Julio Saez-Rodriguez1*

Author Affiliations

1 European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

2 Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA

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BMC Systems Biology 2012, 6:133  doi:10.1186/1752-0509-6-133

Published: 18 October 2012

Additional files

Additional file 1:

Experimental setting for the HepG2 analysis. HepG2 cells were stimulated with the above stimuli in combination with the above-mentioned inhibitors in different combinations. The 16 species mentioned here were then measured using a luminex assay at 30 minutes and 3 hours post stimulation, leading to a total of 136 samples. All species are mentioned with their Uniprot identifiers (capital letters) or common name where applicable (small caps letters).

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Additional file 2:

Summary of results from 3 independent trainings for the HepG2 example. Frequency of selection of each edge in the scaffold model, across all models with a score within 10% of the best scoring model, summarized across 3 independent training runs. The top panel shows the summary for the edges at time 1and the bottom panel shows the equivalent for time 2. For time 1, 13 edges are consistently selected across most (> 80%) of the best performing model, and 24 edges are picked in over 60% of the trained models. A partial redundancy in the effect of some edges explains that a different combination of edges can be picked across different models with limited impact on their scores. At time 2 (lower panel), 5 edges are consistently selected across over 50% of the best scoring models. These lower numbers reflect the fact that the training at time 2 relies on a single trained model as a starting point for both the simulation and the edge search space. Therefore, the family of trained models obtained for each of the training runs explore different search spaces and have different initial conditions.

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Additional file 3:

Technical aspects of the HepG2 analysis. This file provides additional information regarding this analysis, such as the parameters used etc.

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Additional file 4:

Example of results for the HepG2 real data application. A. Previous knowledge network used for this analysis. B. Example of a trained model obtained in one of the optimization round, with a subset of the simulation results obtained with this network (C). For the networks the color codes are as follows: nodes: green=stimulated, red=inhibited, blue=measured, blue with red stroke=measured and inhibited, dashed stroke=compressed; edges (in the trained model in panel B): green=selected at time 1, blue=selected at time 2, grey=not selected in the trained model. In panel C, black continuous lines=data, dashed blue lines=simulation results obtained with the model in B. The background color reflects the goodness of fit of the model to data: green= the chosen Boolean value is closer to the data than the opposite Boolean value (the darker, the closer), red= the chosen Boolean value is further from the data than the opposite Boolean value (the darker, the further).

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Additional file 5:

Exploration of an asynchronous updating scheme for the CNORdt extension. This figure shows the results obtained by training the toy model to data as in Figure 2 but using an asynchronous updating scheme with random firing order of the activation rules, in development for the CNORdt extension. We can see that asynchronous updating adds no new information that is applicable to training the model to data, in this case. For the same conditions as Figure 2, the asynchronous plots show the fraction of simulations (out of 100) where each specified node is switched on (y-axis) after each update of the network (x-axis). The error bars show ± 1 standard deviation of the 100 simulations at each iteration (only 1 in every 10 displayed). In the case of the above model, negative feedback causes oscillations and oscillating nodes average ∼ 0.5. All other nodes stabilize at 0/1. The synchronous plots use the same simulator described in the main text under CNORdt, where all nodes are updated at the same time t according to the state of their input nodes at t-1.

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