Model identification method is briefly explained. Then the sensitivity
analysis is explained in relation to model parameter identification. In view of sensitivity
analysis, the metabolic control analysis (MCA) is explained, followed by its application
to find the limiting pathways for lysine fermentation by Corynebacterium sp. In relation
to flux balance analysis (FBA) and its extension to genome-scale as mentioned in
Chapter 3, the linear programming method is explained for the optimization of a single
objective function under the constraint of stoichiometric equations. A basic approach
for the vector-valued objective function and non-inferior Pareto optimal set is briefly
explained. In relation to model parameter identification, various types of direct and
gradient-based optimum seeking methods are explained. A global search method such
as genetic algorithm (GA) is also explained. Moreover, the optimal operation or optimal
control strategies based on the Maximum principle is explained to find the time optimal
trajectories with some application to ethanol fermentation.
Keywords: Model parameter identification, sensitivity analysis, metabolic control
analysis, MCA, linear programming, non-linear programming, gradient method,
vector valued objective function, Pareto optimal, genetic algorithm, GA,
maximum principle.
