Mathematical Modeling For Phagocyte Transmigration And Reverse Engineering
Kang, Min Gon
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Computational modeling and simulation have been used as an important tool to analyze the behavior of a complex biology system. Typically, the biology system is a complex non-linear system where a large number of components are involved. One of the major obstacles in computational modeling and simulation is to determine a large number of parameters in the mathematical equations representing biological properties of the system.To tackle this problem, we have developed a global optimization method, called Discrete Selection Levenberg-Marquardt (DSLM), for parameter estimation. The method uses a non-linear least square approach to approximate the solution of over-determined systems. For fast computational convergence, DSLM suggests a new approach for the selection of optimal parameters in the discrete spaces, while other global optimization methods such as genetic algorithm and simulated annealing use heuristic approaches that do not guarantee the convergence.As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. The goal of computational modeling is to construct an analyzer to understand the nature of the system. Also, the simulation by computational modeling for phagocyte transmigration provides critical clues to recognize current knowledge of the system and to predict yet-to-be observed biological phenomenon.