EMBER: A Genome-Scale Approach for a Systematic Characterization of Bacterial Metabolic Heterogeneity through the Growth-Adaptation Trade-Off

Microorganisms maintain resilience in fluctuating environments by operating close to a multi-optimality state, balancing growth rate and adaptability. This trade-off dictates bacterial resilience and often complicates metabolic engineering efforts. Addressing it requires identifying specific pathways responsible for diverting metabolic resources away from desired production goals. For this purpose, we introduce EMBER (Exploration of Metabolic trade-offs Based on the mapping of Expression patterns to Reactions), a novel Genome-scale Metabolic Model (GEM) contextualization approach. EMBER integrates transcriptomic data and flux analysis to computationally distinguish between growth-associated reactions (BARs) and adaptive, non-biomass reactions (NBRs). We applied this framework to analyze the metabolic architectures of three diverse and biotechnologically relevant organisms--P. putida, E. coli, and Synechocystis--across various environmental conditions. We revealed marked variability in adaptive resource allocation, with the heterotrophs dedicating substantially more active genes to NBRs (up to 31%) than the photoautotroph Synechocystis (17.5%). Functional analysis showed that BARs consistently supported core metabolism, while NBRs encoded context-specific adaptive functions aligned with the organisms native environment. Analysis of NBR gene expression variability further suggested that P. putida relies predominantly on Bet-Hedging strategies, whereas E. coli employs more regulated Responsive Switching mechanisms. Overall, EMBER offers a powerful systems biology tool to quantify and functionally interpret metabolic heterogeneity. This systematic identification of NBRs will facilitate precise metabolic engineering efforts via reducing unnecessary fitness costs or harnessing the population heterogeneity for deploying complex biotechnological tasks.