Title: Using Groebner Bases to Characterize Data and Improve Model Selection in Network Inference

Abstract: We provide an overview of the problem of network inference in systems biology and highlight contributions using computational algebraic geometry. In particular we describe a correspondence between Groebner bases and minimal discrete models that fit a given data set. We also reveal properties of input data and its associated model space that result in unique models. Implications of this work are guidelines for designing experiments which maximize information content and for determining data sets which yield unambiguous predictions.