Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life history traits and functions may be necessary to predict climate feedbacks due to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here, we developed a Microbial-ENzyme-mediated Decomposition (MEND) model by incorporating microbial physiology and the ability to track multiple isotopes of carbon. Our metadata analysis indicates that the activation energies are significantly higher for lignin-degrading enzymes (ligninases) than for cellulose-degrading enzymes (cellulases); while ligninases have a lower optimal temperature compared to cellulases. This corroborates the carbon-quality-temperature hypothesis that the enzymatic reactions on low-quality C substrates require higher net activation energies. We tested two versions of MEND, i.e., MEND with microbial dormancy and MEND without dormancy, against long-term (270 d) lab incubations of four soils with isotopically-labeled substrates. The MEND with dormancy substantially improved the predictions of microbial biomass over MEND without dormancy, indicating that it is essential to represent dormancy in microbial ecosystem models in order to accurately predict microbial biomass as well as to explain variation in soil organic matter decomposition as a function of time. We also observed that the intrinsic carbon use efficiency (CUE) decreased with increasing temperature by approximately 0.01 per degree Celsius (95% confidence interval: 0.005-0.016 per degree Celsius). The warming effects on soil organic carbon pools could be positive or negative depending on the changes in specific enzyme activities relative to the changes in microbial-enzyme concentrations as well as the temperature sensitivity of CUE. We will use a variety of datasets (e.g., leaf/root litter decomposition) to parameterize MEND and compare different soil biogeochemistry modules in Earth System Models. These efforts should provide essential support to future field- and global-scale simulations and enable more confident predictions of feedbacks between environmental change and carbon cycling.