The presented model is able to correlate mitochondrial morphologies with mitochon-drial qualities and simulates the interaction between the mitochonmitochon-drial network and
for failures in mitochondrial DNA sequences and the distribution of metabolites. Fur-thermore, in order to shorten paths between energy consuming cell sites, mitochondria condensate around the nucleus. This finding agrees with investigations on stressed cells, where mitochondria also aggregate around the nucleus. [63]
During aging procedures of biological processes, the morphological model reveals an on-going fragmentation of mitochondrial clusters and falling mitochondrial qualities. This result is in line with the outcomes of the quality model presented in chapter 2. Addi-tionally, a similar behavior was observed in in-vitro experiments. [38, 39]
Moreover, during aging an increased number of mitochondria performing fission pro-cesses increases the number of fresh mitochondria generated by mitochondrial biogen-esis. As a consequence of enhanced recycling activity, average mitochondrial velocities rise during maturing of the cells in the model. In reality, this mechanism might help mitochondria to aggregate to larger clusters in order to compensate for falling qualities and the fragmentation process caused by aging. However, due to a lack of measurement methods so far there is no experimental data available on the motility of mitochondria.
Future experiments should determine the impact of an alteration of mitochondrial ve-locities on the overall network.
Due to the lack of biological insights some assumptions and definitions of parameter values in the model have to be rather intuitive or simplistic than experimentally deter-mined (see Table 3.1). Results of the model can only qualitatively be compared with experiments but not quantitatively. Similar to the model presented in chapter 2, to date it is not possible to determine the real time scales of the involved biological pro-cesses. Instead, their frequency can only be estimated relatively to each other. [17, 37]
Additionally, both quality parameters, e.g. the health of mitochondrial DNA and the amount of metabolites present in a mitochondrion, can not be measured directly. There-fore, they can only be compared indirectly with experiments by measuringing, e.g. the mitochondrial membrane potential.
The virtual cell in the model has a two-dimensional design, although in reality all cells exist in three dimensions. Thus, the model applies basically to cells that are quasi two-dimensional systems, as e.g. keratinocytes in the stratum granulosum of the epidermis in human skin. [3] In order to use the model to reconstruct the mitochondrial network in more voluminous cells, as e.g. fibroblasts or myocytes, a third dimension has to be included.
Representing mitochondria, the SMUs in the model move freely within the cytoplasm of the virtual cell. This assumption is only an approximation of the quasi-free motion
if they meet within the cytoplasm, except for SMUs that are irreversibly damaged and have health of qh = 0. Experimentally it is not clear, if two meeting mitochondrial clusters always fuse. A fusion probability based on the qualities of involved SMUs could be included. A similar mechanism is already introduced in the quality model in chapter 2.
Furthermore, in the current version of the model, SMUs share metabolites as equal as possible and compensate for each others DNA failures at a maximum level. Although these basic mechanisms are experimentally proven, to date it remains unclear to which extend mitochondria compensate for each others failures and share metabolites when they merge their inner and outer membranes.
Moreover, it is possible, that, contrary to the current design of the model, in reality fresh mitochondria generated by biogenesis do not carry any metabolites and are useless for ATP production until they either have produced enzymes on their own and proteins or have been supplied by other mitochondria following a fusion process.
So far, in the model the maximum number of SMUs is limited by the starting number Nstart and the model does not include an aging procedure for biogenesis. Experiments point to increased biogenetic activities and a growing mitochondrial mass in aged cells.
[36, 64]. This mechanism could be integrated in the model in future.
The ATP consumers are modeled by Mie potentials, that influence the morphological behavior of SMUs. However, the generation of ATP at energy demanding cell sites also consumes metabolites in mitochondria and harms the mitochondrial health, e.g. by oxidative stress produced during processes of the electron transport chain. [65] Hence, it is reasonable to assume, that the qualities of SMUs are coupled to the proximity to ATP consumers. In a more advanced version of the model, the interaction of mitochon-drial qualities and ATP consumers could be integrated by either decreasing the existing quality parameters qh and qm or by introducing a third quality parameter qatp, that represents the amount of ATP carried by a SMU. It would be possible to couple this third quality parameter qatp to the grade of integrity of the two existing ones. With this mechanism, a quality-dependent interaction of SMUs and ATP consumers would also take into account a saturation value of ATP for each consumer. Then, the ATP consumer would stop attracting additional SMUs, if the saturation of the consumer is accomplished by the sourrounding SMUs. With these modifications, the energetic cel-lular architecture could be reconstructed individually for different cell types, such as keratinocytes and fibroblasts. ATP consumers would not only represent general cell functions but could model specific ATP consuming organelles, as e.g. the endoplasmatic
energy demands. Then, the state of the mitochondrial network could be described by a small number of state variables. The velocity of mitochondria could represent a varying temperature and the constant volume would be defined by the shape of the cell. How-ever, in a canonical ensemble, the number of smallest mitochondrial units should be kept at a constant value. Since the mitochondrial mass grows during aging [36], comparison of different mitochondrial networks with canonical states could only be used within a single age group.
Mitochondrial Network in vivo
The multiphoton microscope Dermaninspect (in-vivo intravital tomograph) developed by Beiersdorf AG (Hamburg, Germany) [68] in collaboration with Jenlab (Jena, Germany) [69] was utilized to perform two in-vivo studies on volunteers in order to investigate the mitochondrial network during epidermal differentiation and skin aging. The fundamental concepts of the experimental setup and the tools of analysis of the investigations are presented in the following chapter.