A Case Study: Establishing Boolean Network Model for Macrophage Polarization
Boolean networks have been implemented to characterize biological pathways of cells performing a particular function. Macrophages secrete cytokines to communicate with other nearby cells to pivot an inflammatory response or anti-inflammatory. Using a combination of MATLAB and the BNLearn package in R, we used BNLearn package in R containing a few types of methods that can create a network with the given input file in this study. A combination of constraint, score based and hybrid algorithms such as hill-climbing, Incremental Association, max-min hill-climbing and man-min parent children methods were performed to establish Boolean network mode using temporal profiles of macrophage secretion We established a Boolean model for macrophage polarization post-MI. Based on the network structure, semi-tensor product has been applied to represent logical functions with a linear representation in mathematical equations. A MATLAB package was developed to simulate the evolution of the Boolean network and to illustrate how the evolution of each cytokines being expressed as time progresses. Further, the controllability verification helps to check if using one of the inputs to pathways can lead to the desired output status or not. The dynamic progression of the Boolean model for macrophage polarization helps to understand the inflammatory response of a macrophage cell and implement this research into the medical application.