Inferring cell signaling networks from high-throughput data is a challenging problem in systems biology. Cell signaling plays a central role in cellular processes for constantly adapting environmental stimuli. Several cellular molecules can interact to form a complex signaling network, the subsets of which are associated with one or more signaling pathways, to maintain cellular tissue and organ health.
These networks are not entirely predictable based on the limited information available on the stochastic system. Statistical approaches play a significant role in “network estimation and inference” efforts. More specifically, causal inference is an effective tool for signaling network reconstruction to identify cause-effect relationships among biomolecules.
With the advancement of experimental techniques to quantify protein abundances and post-translational modifications of proteins, it has become feasible to reconstruct the wiring diagram of these networks by statistical means. In particular, mass cytometry allows some 50 different proteins to be monitored at single-cell resolution and hence provides unprecedented data for solving this inverse problem.