The representation of biomedical structure -from cellular components to organisms -in biomedical ontologies is of pivotal impor-tance, as the internal structure of complex structured objects needs to be referenced in the definition of processes, disorders, phenotypes and many other entities. Yet, most of the existing biomedical ontologies do not contain logical axiomatizations for accurately representing the inter-nal structure. We have identified the high importance of mereotopology (parthood, connectedness) for accurate representation in this domain, but the rep-resentation of mereotopological structure can provide challenges for rea-soners. To evaluate the scalability of accurate representation of biomed-ical structure, we have identified design patterns for (i) parthood, both one-sided, two-sided and cardinality restricted, (ii) class disjointness, and (iii) spatial disconnectedness. In order to evaluate the DL reasoning per-formance for these patterns, we have created a T-Box Generator to pro-grammatically generate small and large experimental T-Boxes with dif-ferent reasoning complexities resulting from the relative proportions of the patterns (i) to (iii). Classification times have been measured for different reasoners in their most common application settings. We found that, as expected, reason-ing times increased dramatically with the size and complexity of the generated ontology, and furthermore, even small numbers of cardinality restrictions were a major performance killer.