An Overview Methodology for Writing Suitable Boolean Rules for Protein Signaling Pathways

Abstract

Boolean model elaborates discrete modelling of any biological system with the purpose to study its dynamical evolution. The representative network has been composed of nodes and edges that show the way of interactions between these nodes. The modelling consists of a set of logical functions, known as Boolean functions that represent the interactions between nodes, and are simulated to determine all attractors of the system, and consequently, its stable states are stated as fixed points. In this paper, we give a description of the methodology followed to write Boolean functions. We present two different Boolean models constructed by these two methods and the differences shown in the results they simulate. In a situation where experimental data are missing, the functions have been usually written under prediction and assumptions made for this occasion, because the path followed by the information to jump from one node to another was considered mandatory for the first Boolean model. Differently, in the second Boolean model activators and inhibitors are considered separately without any restriction, as in the first method. Here, the type of interactions was considered important, because we are interested to know only what flows in and out from any target node. The methodology has been applied firstly in a hypothetical representative system and then in four real signalling pathways. We have identified many differences in the simulated fixed points and concluded that the second model offers more results for further analysis. Consequently, there is a higher probability that we find, through second Boolean modelling, more suitable stable states that correspond to the biology.