a parameter estimation approach for non-linear systems biology models using spline approximation
Mathematical models for revealing the dynamics and interactions properties of biological systems play an important role in computational systems biology. The inference of model parameter values from time-course data can be considered as a "reverse engineering" process and still one of the most challenging tasks. It is worth to develop parameter estimation methods which are robust against noise, efficient in computation and flexible enough to meet different constraints. Parameter estimation method of combining spline theory with Nonlinear Programming (NLP) is developed. The method removes the need for ODE solver during the identification process. Our analysis shows that the augmented cost function surface used in the proposed method is smoother than the original one; which can ease the optima searching process and hence enhance the robustness and speed. Moreover, the core of our algorithms is NLP based, which is very flexible and consequently additional constraints can be added/removed easily. Our results confirm that the proposed method is both efficient and robust.
parameter estimation spline nonlinear programming