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  • 1.
    Wang, Mingliang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.
    Risuleo, Riccardo Sven
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.
    Chotteau, Veronique
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. KTH, Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.
    Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes2020In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 133, article id 106671Article in journal (Refereed)
    Abstract [en]

    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.

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