Genome-scale metabolic networks in time and space. Issue 8 (April 2018) - Øyås, Ove; Stelling, Jörg
Abstract: Constraint-based models (CBMs) are key tools for elucidating the behavior of genome-scale metabolic networks, but the assumption of steady state hinders their application to spatiotemporally varying and multicellular systems. Models that integrate CBMs with kinetics to allow dynamic simulation through dynamic flux balance analysis (DFBA) can circumvent this problem as well as the limitations of purely kinetic models. With many technical barriers for DFBA overcome in recent years, applications traditionally focused on metabolic engineering have expanded to address problems such as evolution of microbial communities, functions of biomedically relevant biofilms, and diet effects on Parkinson's disease. By addressing substantial computational challenges, we expect that such hybrid metabolic models will pave the way towards whole-cell modeling. Highlights: Models coupling static and dynamic aspects enable the analysis of multicellular metabolism. Recent developments of DFBA were critical for opening novel application areas. Computational challenges remain on the way to detailed whole-cell or -organism models.