Modeling cytokine signaling pathways for the study of autoimmune disease
Year: 2014
Type: dissertation
Abstract: Systems Biology opens new frontiers in the studies of complex diseases such as Multiple Sclerosis (MS). The questions that couldn’t be addressed before due to lack of understanding and methodological base, such as the molecular mechanism of action of many drugs on the signaling pathways, were resolved using new Systems Biology vision of nature. Signaling pathways have dual biological and mathematical nature. Modern Systems Biology developed various methods to model signaling pathways and predict their behavior in different conditions. \nThis dissertation is focused on some of the key molecular mechanisms of signal transduction in MS development and progression, which are important in order to explain the mechanism of action of the common MS drugs. \nMy main hypotheses are based on the assumption that the most important art of the biological system are the connections between molecules rather than the molecules themselves (e.g. edges rather than nodes of the model graph). For example, the immune cell subtypes have different response to the external stimulus because the molecules regulate different activities of each other inside the cell rather than the components of the cells are different. Another examples of it are the kinetic changes driven by the changes in the translocation activity of the Stat1 protein. \nTo prove my hypothesis, I developed two different mathematical models of IFNbeta pathway: Boolean and ordinary differential equations (ODE). The combination of two modeling approaches allowed us to look at the same pathway from two perspectives: in connection with other related pathways and as a kinetic system. We identified oscillatory and damped oscillatory regimes in the IFNbeta signaling and the key sensitive parameters, which determine the switch of the regimes. Both model were validated experimentally and leaded to several predictions, which could be important for the development of new drugs or drug combination. For example, the bifurcation analysis of the kinetic model revealed the importance of the features of the nuclear translocation of the Stat1 protein for the correct functioning of the signaling pathway. Sgk/Akt-Foxo3a is another pathway described, modeled and validated in my dissertation. The nuclear translocation is a known key element of this system, but we focused on the on/off circuit mechanisms and the importance of the combination of different phosphorylation sites for signal transduction. \nThe main outcomes of this work are: \n1.\tNew models of IL6, IFNbeta and Akt/Sgk signaling pathways \n2.\tPredicted prevalence of translocation parameters over the phosphorylation rates on the IFNbeta pathway \n3.\tNew method of the application of the Boolean model workflow to the clinical data \nAs a conclusion, the Systems Biology is a powerful tool to predict new properties of the biological systems, which can be used in clinical practice, such as dynamical biomarkers or differential signal transduction. Thus, Systems biology provides a new approach to search for new treatments and biomarkers of autoimmune diseases.
Author: Inna Pertsovskaya
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W637076172