A global resource constrained model to predict metabolic flux dynamics in fluctuating environments

Environmental changes often induce global variations on bacterial gene expression, frequently accompanied by alterations in growth rates. Integrating non-metabolic cellular processes, such as gene expression and macromolecule synthesis, with metabolic modeling remains a significant challenge in systems biology due to the lack of mechanistic representation of gene regulation. Here, we introduce a novel constraint-based modeling framework called dynamic Constrained Allocation Flux Balance Analysis (dCAFBA), which comprehensively integrates metabolism, cellular resource allocation, and gene regulation. We employ a quasi-steady-state assumption, positing that reaction fluxes achieve balance at each time step, adapting more rapidly than protein synthesis and growth dilution. This approach enables the prediction of reaction flux dynamics and protein allocation necessary to achieve cellular objectives, such as optimizing growth during various growth shifts (including carbon, amino acid, and transcriptional signal), without detailing molecular gene regulations. Our model offers a new method for interpreting proteome allocation and cellular metabolism in complex and transient environments, providing mechanistic insights valuable for metabolic engineering.