PARAMETER ESTIMATION FOR BIOLOGICAL MODEL WITH SPIRAL DYNAMICS OPTIMIZATION - MICHELLA (NIM: 10113049), LEVINA
Modeling and simulation of a biological phenomenon may involve a system of differential equations which describes the interactions between its biological components. Biological models usually depend on several parameters and initial conditions. Improper parameter values will cause misleading result in simulation. This can be prevented by estimating the unknown parameter values by fitting the model to experimental data. Parameter estimation relates to minimizing an objective function which evaluates the difference between model prediction and experimental data. Generally, the biological model involves the system of nonlinear differential equation. Parameter estimation problem will be brought to a nonlinear optimization problem. Therefore, the problem of parameter estimation will be solved numerically. In this thesis, parameter estimation for the biological model will be solved numerically with Spiral Dynamics optimization. In addition to Spiral Dynamics, Nonlinear Least Square and Genetic Algorithm method will be used to estimate the parameters of the biological model as a comparison. In terms of computing, Spiral Dynamics produces pretty good parameters with the fastest computation time.