A new computational tool was developed, for model-based analysis of the endocytosis and exocytosis mechanisms involved in nanocarrier delivery. This was a hypothetical study because current data are insufficient to identify the underlying process model.
The detailed case studies represent appropriate examples for useful applications of a quite hypothetical model. It allows the study of the individual mechanisms, as well as the synergistic and antagonistic effects of their combinations. It helps to understand how the possible limiting transportations and transformations, as well as the inherent degradations, limit the utilization of the injected drug. In addition, by switching on and off the respective processes, we can evaluate the beneficial or harmful effects of some pathways, like nanocarrier or drug-containing nanocarrier exocytosis.
The applied methodology was flexible and effective, permitting one to describe and run complex process models without any mathematical and computational assistance. The question is whether constructive applications can compensate for the possible malfunctions, caused by the limiting assumptions. Our answer is yes.
The development of this computational model is a valid example for the qualitative identification and validation, organized by the dialogue between field and model experts with the computational model. As such, it can help to organize new round of experiments.
We can state that the future of the computer assisted biosystem related, biotechnological and biomedical studies depends highly on this kind of interactive collaborations. Direct Computer Mapping (DCM) seems to be an effective tool for the organization of these collaborations.
Looking at the Chapters of this Volume it is obvious that the majority of the contributions have very sophisticated and deep experimental background, related to various specific details. From an engineering point of views, they are well-elaborated pieces of a puzzle. However, our simplified, but systematic computational model emphasizes the hypothetical big picture. Similarly to bridge building, in computational systems biology we have to construct the bridge from both sides and, hopefully, to meet in the middle.