The growing agreement that soil organic matter (SOM) is an essential factor to predict and optimise a wide range of soil ecosystem services, has made the lack of generally accepted mechanistic modelling tools for SOM degradation a pressing matter. In this study, we developed a state of the art mechanistic toolbox which embodies the core of SOM degradation, while maintaining a model structure that allows flexible addition of interactions with other soil components. The theoretical framework of the toolbox is an aerobic reaction network where SOM is defined as a mixture of known organic compounds from plant and microbial origin, that interact with microorganisms in a non-limiting aqueous environment. Simulations of the toolbox show that the bulk properties of SOM can be interpreted as weighted averages of the properties of individual organic compounds. The downside of implementing a novel theoretical approach in the toolbox, is that some crucial parameter values are poorly documented in literature. To tackle this issue, we incorporated a Bayesian inference tool, which is capable of selecting an experimental design that makes optimal use of the inherent model structure to maximise the future parameter identifiability. The toolbox shows promising outlooks to both a) increase the acceptance of the state of the art insights in SOM degradation by reinterpreting older experimental findings, while b) further enhancing mechanistic modelling of interaction processes between SOM and other soil components to, ultimately, make accurate predictions of SOM degradation within a complex soil environment.