Incremental Model Identification of Bio-processes from Data: Application to Microbial Production of Hyaluronic Acid

Abstract

The development of reliable kinetic models of bioprocesses from data is a challenging task. In this work, a systematic approach to develop a kinetic model of bioprocess involving single biomass from concentration data is proposed without imposing any kinetic model a priori. The proposed incremental model identification approach decomposes the model-building task into a set of sub-tasks such as determining the yield coefficients and maintenance coefficient, specific growth rate structure identification, and parameter estimation. It is shown that the proposed approach allows identifying the mechanism of product formation. The proposed approach is applied to the microbial production of Hyaluronic acid (HA), an important biopolymer, using a recombinant Lactococcus lactis MKG6. An unstructured kinetic model is developed for the HA production from data. It is shown that HA production is a growth-associated process. Further, the specific growth rate of HA production is identified from a set of rate candidates. It is revealed that the specific growth rate in the HA production follows the non-competitive HA inhibition model. The parameters obtained by the incremental identification are further refined to obtain statistically optimal estimates using the simultaneous model identification. Validation of the identified kinetic model of HA production on new experimental data shows that the proposed approach leads to a reliable kinetic model with the optimal parameter estimates.