The topology of chemical reaction networks is commonly treated as a static structure. This might be sufficient if substrate concentrations and kinetic parameter values exclusively determine the behaviour of all considered reactions. In contrast, numerous phenomena observed in life sciences imply a different nature by dynamical composition of reaction schemes. Single reactions or functional groups of reactions (modules) become activated or deactivated on the fly by external signals such as light intensity. In other scenarios, reactions emerge or disappear while modules can connect to each other or disconnect due to presence or absence of corresponding trigger signals. Resulting dynamical reaction network structures turn out to be a fascinating application of P systems which facilitates detailed in-silico simulation studies and hence an easier understanding and prediction of complex biological systems. We introduce a P meta framework for resulting polymorphic processes. A case study dedicated to photosynthesis in plants demonstrates its usefulness beyond pure employment of ordinary differential equations by consideration of events, non-differentiable external trigger signals, and thresholds which collaterally modify the underlying reaction scheme.