مدل سازي ديناميكي سلول هاي پلي پلوئيد سرطاني براي شناسايي پروتئين هاي موثر در اهداف دارويي

According to the latest data from the World Health Organization, cancer is recognized as the second leading cause of death worldwide. Despite extensive research aimed at finding a complete cure for cancer, we still face drug resistance from cancer cells in various treatments. To address this problem, systems biology has been proposed as an interdisciplinary approach in biology an‎d medicine that utilizes technology, mathematical computations, physics, an‎d modeling to understan‎d the structure an‎d function of living systems. Biological network modeling serves as a method for systematically collecting an‎d integrating biological data, enabling the testing an‎d validation of fundamental principles of biological mechanisms. One approach within systems biology is the creation of dynamic equations representing the function an‎d nature of cancer. In particular, ordinary differential equations are widely used for a comprehensive understan‎ding of the complex behavior an‎d mechanisms of systems such as cancer in biology. A type of cancer cell recently linked to cancer is polyploid cells, which have a multinucleated structure an‎d are resistant to various treatments. These cells have recently been recognized as a major factor in the survival of cancer cells after treatment. In this thesis, the ultimate goal is to model the proteomic network of these cells using real-world data an‎d to identify effective proteins in polyploid cells. First, by studying various types of polyploid cells, cancer-related polyploid cells are identified. Then, experimental data obtained from these cells on a proteomic scale is searched. By extracting an‎d analyzing this data using systems biology software, the concentration an‎d expression levels of various proteins present in these cells are obtained. By inputting the expression intensity of each of these proteins into a differential equation model building tool, a large-scale ordinary differential equation model of these cancer cells is constructed. This model requires parameter estimation an‎d optimization. After parameter optimization, the accuracy of the obtained equations is initially assessed by comparing the temporal response of the equations with experimental data, using numerical simulation of the set of equations. Finally, using sensitivity analysis with an algorithm written specifically for the large-scale data of this thesis, effective proteins an‎d essential parameters of the set of equations were obtained. In the end, this set of effective proteins has been reported to the Regenerative Medicine Center of Isfahan University for use in pharmaceutical cancer treatment purposes.

جعفر قيصري

مرضيه كمالي , مريم ذكري