  With the assumption that the effect is a mathematical function of the cause
in a causal relationship, FunChisq, a chi-square test defined on a
non-parametric representation of interactions, infers network topology
considering both interaction directionality and nonlinearity. Here we show that
both experimental and in silico biological network data suggest the importance
of directionality as evidence for causality. Counter-intuitively, patterns in
those interactions effectively revealed by FunChisq enlist an experimental
design principle essential to network inference -- perturbations to a
biological system shall make it transits between linear and nonlinear working
zones, instead of operating only in a linear working zone.
