The field of systems biology thrives upon the use of models to organize biological knowledge and make predictions of complex processes that are hard to measure. When attempting to generate model descriptions for metabolic systems, one arrives at a crossroads. A variety of mathematical explanations are available for metabolic data with varying degrees of resolution from simple to complex. Biological modelers often rely upon subjective arguments to choose one framework over another. While there is no universal rule to determine the absolute utility of a model, certain metrics founded on information theoretical principles, demonstrate promise in providing a coherent, rational, and objective basis for addressing this model selection problem in systems biology. A model seeks to capture the regularity in biological data. Models that best capture regularity in data without excessive complexity are the most useful for applications in optimization and control. To demonstrate the efficacy of such an approach, several metabolic model selection scenarios are investigated. This work develops the argument that information theoretic model selection metrics should be extended to nonnested model comparison applications in systems biology. It also makes a novel comparison of kinetic, constraint-based, and cybernetic models of metabolism based not only on model accuracy, but also model complexity. The results show the strengths of lumped hybrid cybernetic model (L-HCM) and flux balance analysis (FBA) for applications in steady state flux prediction. Also, the hybrid cybernetic model's (HCM) merit in the modeling of dynamic changes in fluxes is also established.