The state-of-the-art understanding of activated sludge processes as summarized in activated sludge models (ASMs) predicts an instantaneous increase in the biomass activity (which is measured, e.g., by the corresponding respiration rate OUR, NUR, etc.) under sudden substrate concentration changes. Experimental data (e.g., short-term batch respiration experiments under aerobic or anoxic conditions) collected for the calibration of the dynamic models (ASMs) often exhibit a transient phenomenon while attaining maximum activity, which cannot be explained by the current understanding of the activated sludge process. That transient phenomenon exhibits itself immediately upon addition of a substrate source to an endogenously respiring activated sludge sample and it usually takes a few minutes until the activated sludge reaches its maximum possible rate under given environmental conditions. This discrepancy between the state-of-the-art model and the experimental data is addressed in detail in this investigation. It is shown that the discrepancy is not caused by an error in the experimental set-up/data but it is rather due to model inadequacy. Among the hypotheses proposed, it appears that this transient response of the activated sludge most likely results from the sequence of intracellular reactions involved in substrate degradation by the activated sludge. Results from studies performed elsewhere with pure cultures (S. cerevisae and E. coli) support the hypothesis. The transient phenomenon can be described by a dynamic metabolic network model or by a simple first-order model, as adopted in this study. The transient phenomenon occurring in short-term batch respiration experiments is shown to interfere severely with parameter estimation if not modeled properly (2.8%, 11.5%, and 16.8% relative errors [average of three experiments] on Y(H), micro(maxH), and K(S), respectively). Proper modeling of this transient phenomenon whose time constant is on the order of minutes (1 to 3 min) is expected to contribute fundamentally to a better understanding and modeling of Orbal, carousel, and SBR-type treatment plants with fast-alternating process conditions, although such studies are beyond the scope of this report.