Identification of nested biological kinetic models using likelihood ratio tests

Abstract

Dynamic modeling of bioprocesses is of fundamental importance for optimization and control. The underlying macroscopic models, i.e., reaction scheme stoichiometry and kinetics, have to be derived and identified from experimental data. This study addresses the problem of determining the kinetics, and proposes a systematic procedure based on the use of decision graphs collecting well-established kinetic laws, and organizing them according to their complexity (i.e., number of unknown parameters). An algorithm for selecting the most likely kinetic structures, starting from the simplest models in the decision graphs, is developed based on the concepts of nested models and likelihood ratio tests. The potential of the procedure is illustrated with the determination of the synthesis rate of a biopolymer from the mutant strain Azotobacter vinelandii utilizing glucose.