Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes

• Published in: Computers & chemical engineering Vol. 133; p. 106671
• Main Authors: Wang, Mingliang, Risuleo, Riccardo Sven, Jacobsen, Elling W., Chotteau, Véronique, Hjalmarsson, Håkan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 02.02.2020
• Subjects: Gaussian process; Kinetics; Macroscopic modeling; Model selection; Monod model; Nonlinear systems; Parameter estimation; Monod model; Parameter estimation; Gaussian process; Model selection; Macroscopic modeling; Kinetics; Nonlinear systems
• ISSN: 0098-1354; 1873-4375; 1873-4375
• DOI: 10.1016/j.compchemeng.2019.106671

In biological systems, nonlinear kinetic relationships between metabolites of interest are modeled for various purposes. Usually, little a priori knowledge is available in such models. Identifying the unknown kinetics is, therefore, a critical step which can be very challenging due to the problems of (i) model selection and (ii) nonlinear parameter estimation. In this paper, we aim to address these problems systematically in a framework based on multilinear Gaussian processes using a family of kernels tailored to typical behaviours of modulation effects such as activation and inhibition or combinations thereof. Using one such process as a model for each modulation effect leads to a much more flexible model than conventional parametric models, e.g., the Monod model. The resulting models of the modulation effects can also be used as a starting point for estimating parametric kinetic models. As each modulation effect is modeled separately, this task is greatly simplified compared to the conventional approach where the parameters in all modulation functions have to be estimated simultaneously. We also show how the type of modulation effect can be selected automatically by way of regularization, thus by-passing the model selection problem. The resulting parameter estimates can be used as initial estimates in the conventional approach where the full model is estimated. Numerical experiments, including fed-batch simulations, are conducted to demonstrate our methods.