Resolution:
## Figure 2.
Simulation schemes in the CellNOptR and add-ons packages. Adapted from[40]. This network is a simplified version of a realistic toy example from
[40], used to generate simulated data (triangles). We show a subset of the results of
training this model to data using each of the logic formalisms available through our
packages (dashed red lines). The model contains canonical pathways downstream of EGF
and TNFα. The data includes: (i) a slow negative feedback from ERK to SOS-1 leading to a transient
activation of ERK, (ii) a feedback from NFκB to its inhibitor IκB, leading to oscillations of NFκB, and (iii) a partial activation of p38 under combined EGF-TNFa stimulations. The
CellNOptR simulation scheme (Boolean, steady state) captures the activation of ERK upon EGF
stimulation (black edges EGF - SOS-1 - ERK) but not its transient nature. The Boolean
with two steady states version does capture the transient ERK activation (i.e. both
the black path between EGF and ERK and the negative ’AND’ gate when both EGF and ERK
are activated, blue edges) but not the NFkB oscillations and p38 partial activation.
With the discrete time updating scheme with Boolean state from CNORdt, we capture both the transient activation of ERK and the NFκB oscillations(orange edges) but not the partial activation of p38. CNORfuzzy implements a continuous representation of states but with a single steady state.
Thus, it captures the partial activation of p38 (pink edges) but not the behaviors
of ERK and NFκB. CNORode is based on a continuous representation of both time and state, which captures the
behaviors of ERK, p38 and NFκB (green edges). Depending on the available data and the suspected behaviors to capture,
different logic formalisms are more appropriate. Dashed lines=time points used for
steady states. Color of model edges: black=captured by all approaches, blue = CellNOptR(2t),
orange = CNORdt, pink=CNORfuzzy, green=CNORode.
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