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

Deciphering Interactions in Causal Networks without Parametric Assumptions