During the investigation of protein protein interaction, there is a lot of data generated after two-hybrid, affinity purification and mass spectrometry.
On another hand, protein interaction networks could be extracted from literature by scientific curators or some text mining techniques.
These networks are usually displayed in node-edge graph. But these graph do not help to understand the specific cellular responses are activated by which stimuli. Mathematical models could do this job, but usually involves a lot parametres and the estimation of the parametres is tedious and sometimes clueless.
"Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction" is using logic model to represent the interaction network and then train it against experimental data. After this calibration, it was found that the prediction power of model is improved.
Now the research followed up this paper is going to be able to digest the automatically generated models (in BioPAX etc.). And it would be further better, if this research could be combined with mathematical models and help to calibrate the mathematical models.
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