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Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes
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.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-2831-2909
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.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. KTH, Centres, Centre for Advanced BioProduction by Continuous Processing, AdBIOPRO.ORCID iD: 0000-0002-5370-4621
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2020 (English)In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 133, article id 106671Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 133, article id 106671
Keywords [en]
Gaussian process, Model selection, Parameter estimation, Monod model, Kinetics, Macroscopic modeling, Nonlinear systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-266714DOI: 10.1016/j.compchemeng.2019.106671ISI: 000504755000017Scopus ID: 2-s2.0-85076153731OAI: oai:DiVA.org:kth-266714DiVA, id: diva2:1387596
Note

QC 20200122

Available from: 2020-01-22 Created: 2020-01-22 Last updated: 2020-01-22Bibliographically approved

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Risuleo, Riccardo SvenJacobsen, Elling W.Hjalmarsson, Håkan

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Wang, MingliangRisuleo, Riccardo SvenJacobsen, Elling W.Chotteau, VeroniqueHjalmarsson, Håkan
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Decision and Control Systems (Automatic Control)Centre for Advanced BioProduction by Continuous Processing, AdBIOPROIndustrial Biotechnology
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