  Biological systems (among others) may respond to a large variety of distinct
external stimuli, or signals. These perturbations will generally be presented
to the system not singly, but in various combinations, so that a proper
understanding of the system response requires assessment of the degree to which
the effects of one signal modulate the effects of another. This paper develops
a pair of structural metrics for sparse differential equation models of complex
dynamic systems and demonstrates that said metrics correlate with proxies of
the susceptibility of one signal-response to be altered in the context of a
second signal. One of these metrics may be interpreted as a normalized arc
density in the neighborhood of certain influential nodes; this metric appears
to correlate with increased independence of signal response.
