  Design of experiments is a branch of statistics that aims to identify
efficient procedures for planning experiments in order to optimize knowledge
discovery. Network inference is a subfield of systems biology devoted to the
identification of biochemical networks from experimental data. Common to both
areas of research is their focus on the maximization of information gathered
from experimentation. The goal of this paper is to establish a connection
between these two areas coming from the common use of polynomial models and
techniques from computational algebra.
