A useful approach to model large-scale signaling networks consists on training Boolean logic models from prior knowledge and dedicated experimental data.
Throughout, miniature scale signals childhood memories, and architectural details are sampled as dream stage-sets.
The measurement system can input the interferometer signal by the encoder signal or the scale signal, and input the encoder or scale data by the interferometer signal.
Each consisted of the GC-RMA log2 scale signal intensity data for the paired treatments (e.g. Upper and Mock) (sample size = 3 × 24 = 72).
Sub-genome scale combined signaling, gene expression and metabolism models have been developed [ 51].
Such a minimal model based approach to elucidate design principles of cell signaling pathways is very general and can also be applied to study other large-scale signaling systems.
Qualitative (ie, parameter-free) models enable the computational representation and analysis of even large-scale signaling networks.
Recently, a method called CCELL was proposed, for cell-scale signaling network inference over a predefined timescale using time series immunoprecipitation data based on Bayesian compressive sensing [ 123].
The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.
