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The ‘architecture’ of a biochemical system can be inferred from high-throughput data, but the corresponding model consists of numerous unknown kinetic parameters. The system phenotype becomes clear only when these parameters are specified; even simple models can exhibit many phenotypes given different parameter choices. Thus, current approaches to determining the phenotypic repertoire of a model typically focus first on finding parameter values for the underlying biochemistry, typically through a mixture of ad-hoc experimentation and computationally inefficient high-dimensional numerical search. While this strategy has been used to characterize simple systems, the attempt to derivemechanistic understanding of more complex systems, even of moderate size, has remained elusive. I will propose a fundamentally different modeling strategyin which we first determine the space of possible phenotypes for a given architecture and then predict parameter values for their realization; these predictions can then guide experimentation and further numerical analysis. This ‘phenotype-centric’ paradigm combines several innovations with the potential torevolutionize our understanding of complex biochemical systems. In this talk I will describe recent progress toward the realization of this potential and some of the remaining challenges.