Abstract
Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. We develop dynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. Our dynamicME correctly predicted the substrate utilization hierarchy on mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus present two methods to calibrate ME models, specifically using time-course measurements such as from a (fed-) batch culture. Overall, dynamicME and the methods presented provide novel methods for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
