Like the Type A ModelBrick, a receptor pool cycles between Aoff (available), BAon (bound to signal B and actively signaling), and BAin (inactivated). But here the binding step uses a different, nonlinear rate law — one in which the net flux into BAon depends on how far the signal B is from the already-occupied fraction (BAon+BAin). That single change makes this "Type B" variant behave very differently from Type A once the signal steps back down. Apply the same up-then-down staircase used throughout this series and watch what happens to the inactivated pool.
Drag across the plot to inspect values at a given time. Adjust the controls below and watch this graph update.
Compare this with the Type A ModelBrick: there, BAin only ever grows, and the responsive pool ends near 0. Here, BAin rises during each up-step but drains back out during the step-down phase, and the responsive pool is essentially fully restored by the end of the run — a recoverable, self-limiting form of inactivation rather than a permanent sink. (Watch BAon closely during recovery: it dips slightly negative for an instant — an expected feature of this reduced rate law, not an error — which is exactly what lets BAin flow back out.)
Reaction network: Aoff + B ↔ BAon, rate = k1(1−u)(B−u) − k2BAon, where u = BAon+BAin · BAon → BAin, rate = kinactBAon
This ModelBrick comes from the same VCell/BioModels family as the antithetic (X-mediated) perfect-adaptation model, the direct negative-feedback model, the incoherent feedforward model, and the Type A state-dependent-inactivation model. It uses the same three-state topology as Type A (Aoff ↔ BAon → BAin), but the binding step is governed by a different, nonlinear rate law: the net forward flux is proportional to k1(1−u)(B−u), where u = BAon+BAin is the fraction already occupied (bound or inactivated). Algebraically this collapses the SBML file's nine-term polynomial kinetic law into that compact product — both forms were checked numerically against each other while building this page and agree to machine precision. Because the driving force is B − u rather than B alone, flux reverses direction once the occupied fraction exceeds the signal: as B is stepped back down, this rate law actively drains the inactivated pool BAin back toward Aoff, so — unlike Type A — the responsive pool is essentially fully restored by the end of the run. B is stepped up in four increments of 0.2 and then back down in four decrements of 0.2, exactly as in the earlier models, so the two "state-dependent inactivation" ModelBricks can be compared directly. Originally built and simulated in VCell and exported as SBML.