Modeling and Parameter Estimation in Biological Applications

Abstract

Biological systems, processes, and applications present modeling challenges in the form of system complexity, limited steady-state availability, and limited measurements. One primary issue is the lack of well-estimated parameters. This thesis presents two contributions in the area of modeling and parameter estimation for these kinds of biological processes. The primary contribution is the development of an adaptive parameter estimation process that includes parameter selection, evaluation, and estimation, applied along with modeling of cell growth in culture. The second contribution shows the importance of parameter estimation for evaluation of experiment and process design.