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- Theodor-Boveri-Institut für Biowissenschaften (3) (remove)
Once biological systems are modeled by regulatory networks, the next step is to include external stimuli, which model the experimental possibilities to affect the activity level of certain network’s nodes, in a mathematical framework. Then, this framework can be interpreted as a mathematical optimal control framework such that optimization algorithms can be used to determine external stimuli which cause a desired switch from an initial state of the network to another final state. These external stimuli are the intervention points for the corresponding biological experiment to obtain the desired outcome of the considered experiment. In this work, the model of regulatory networks is extended to controlled regulatory networks. For this purpose, external stimuli are considered which can affect the activity of the network’s nodes by activation or inhibition. A method is presented how to calculate a selection of external stimuli which causes a switch between two different steady states of a regulatory network. A software solution based on Jimena and Mathworks Matlab is provided. Furthermore, numerical examples are presented to demonstrate application and scope of the software on networks of 4 nodes, 11 nodes and 36 nodes. Moreover, we analyze the aggregation of platelets and the behavior of a basic T-helper cell protein-protein interaction network and its maturation towards Th0, Th1, Th2, Th17 and Treg cells in accordance with experimental data.
A fine balance of regulatory (T\(_{reg}\)) and conventional CD4\(^+\) T cells (T\(_{conv}\)) is required to prevent harmful immune responses, while at the same time ensuring the development of protective immunity against pathogens. As for many cellular processes, sphingolipid metabolism also crucially modulates the T\(_{reg}\)/T\(_{conv}\) balance. However, our understanding of how sphingolipid metabolism is involved in T cell biology is still evolving and a better characterization of the tools at hand is required to advance the field. Therefore, we established a reductionist liposomal membrane model system to imitate the plasma membrane of mouse T\(_{reg}\) and T\(_{conv}\) with regards to their ceramide content. We found that the capacity of membranes to incorporate externally added azide-functionalized ceramide positively correlated with the ceramide content of the liposomes. Moreover, we studied the impact of the different liposomal preparations on primary mouse splenocytes in vitro. The addition of liposomes to resting, but not activated, splenocytes maintained viability with liposomes containing high amounts of C\(_{16}\)-ceramide being most efficient. Our data thus suggest that differences in ceramide post-incorporation into T\(_{reg}\) and T\(_{conv}\) reflect differences in the ceramide content of cellular membranes.
Like human Th1 cells, mouse Th1 cells also secrete IFN‐γ upon stimulation with a superagonistic anti‐CD28 monoclonal antibody (CD28‐SA). Crosslinking of the CD28‐SA via FcR and CD40‐CD40L interactions greatly increased IFN‐γ release. Our data stress the utility of the mouse as a model organism for immune responses in humans.