  As metabolomics datasets are becoming larger and more complex, there is an
increasing need for model-based data integration and analysis to optimally
leverage these data. Dynamical models of metabolism allow for the integration
of heterogeneous data and the analysis of dynamical phenotypes. Here, we review
recent efforts in using dynamical metabolic models for data integration,
focusing on approaches that are not restricted to steady-state measurements or
that require flux distributions as inputs. Furthermore, we discuss recent
advances and current challenges. We conclude that much progress has been made
in various areas, such as the development of scalable simulation tools, and
that, although challenges remain, dynamical modeling is a powerful tool for
metabolomics data analysis that is not yet living up to its full potential.
