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Zhang, L., Wang, M., Castan, A., Stevenson, J., Chatzissavidou, N., Hjalmarsson, H., . . . Chotteau, V. (2020). Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.. Metabolic engineering, 57, 118-128
Open this publication in new window or tab >>Glycan Residues Balance Analysis: A novel model for the N-linked glycosylation of IgG produced by CHO cells.
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2020 (English)In: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184, Vol. 57, p. 118-128Article in journal (Refereed) Published
Abstract [en]

The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation patterns compared to the cultures with glucose. Furthermore, the growth rate and IgG productivity were similar in all the conditions. When the cells were cultured with galactose alone, lactate was fed as well to be used as complementary carbon source, leading to cell growth rate and IgG productivity comparable to feeding the other sugars. The data of the glycoprofiles were used for training the model, and then to simulate the glycosylation changes with varying the concentrations of mannose and galactose. In this study we showed that the GReBA model had a good predictive capacity of the N-linked glycosylation. The GReBA can be used as a guidance for development of glycoprotein cultivation processes.

Keywords
CHO cells, IgG, Mathematical modelling, N-linked glycosylation, Pseudo-perfusion
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-261092 (URN)10.1016/j.ymben.2019.08.016 (DOI)000506206200012 ()31539564 (PubMedID)2-s2.0-85074776776 (Scopus ID)
Note

QC 20191112

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2020-05-14Bibliographically approved
Wang, M., Risuleo, R. S., Jacobsen, E. W., Chotteau, V. & Hjalmarsson, H. (2020). Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes. Computers and Chemical Engineering, 133, Article ID 106671.
Open this publication in new window or tab >>Identification of nonlinear kinetics of macroscopic bio-reactions using multilinear Gaussian processes
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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
Keywords
Gaussian process, Model selection, Parameter estimation, Monod model, Kinetics, Macroscopic modeling, Nonlinear systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-266714 (URN)10.1016/j.compchemeng.2019.106671 (DOI)000504755000017 ()2-s2.0-85076153731 (Scopus ID)
Note

QC 20200122

Available from: 2020-01-22 Created: 2020-01-22 Last updated: 2020-01-22Bibliographically approved
Ferizbegovic, M., Umenberger, J., Hjalmarsson, H. & Schon, T. B. (2020). Learning Robust LQ-Controllers Using Application Oriented Exploration. IEEE Control Systems Letters, 4(1), 19-24, Article ID 8732482.
Open this publication in new window or tab >>Learning Robust LQ-Controllers Using Application Oriented Exploration
2020 (English)In: IEEE Control Systems Letters, ISSN 2475-1456, Vol. 4, no 1, p. 19-24, article id 8732482Article in journal (Refereed) Published
Abstract [en]

This letter concerns the problem of learning robust LQ-controllers, when the dynamics of the linear system are unknown. First, we propose a robust control synthesis method to minimize the worst-case LQ cost, with probability 1-δ , given empirical observations of the system. Next, we propose an approximate dual controller that simultaneously regulates the system and reduces model uncertainty. The objective of the dual controller is to minimize the worst-case cost attained by a new robust controller, synthesized with the reduced model uncertainty. The dual controller is subject to an exploration budget in the sense that it has constraints on its worst-case cost with respect to the current model uncertainty. In our numerical experiments, we observe better performance of the proposed robust LQ regulator over the existing methods. Moreover, the dual control strategy gives promising results in comparison with the common greedy random exploration strategies.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Identification, machine learning, robust control, Budget control, Control system synthesis, Identification (control systems), Learning systems, Linear systems, Numerical methods, Uncertainty analysis, Application-oriented, Exploration budget, Exploration strategies, Model uncertainties, Numerical experiments, Robust control synthesis, Robust controllers, Robust LQ controller, Controllers
National Category
Control Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-263457 (URN)10.1109/LCSYS.2019.2921512 (DOI)2-s2.0-85067870073 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20191205

Available from: 2019-12-05 Created: 2019-12-05 Last updated: 2019-12-05Bibliographically approved
Rodrigues, D., Abdalmoaty, M. & Hjalmarsson, H. (2020). Toward Tractable Global Solutions to Bayesian Point Estimation Problems via Sparse Sum-of-Squares Relaxations. In: The 2020 American Control Conference: . Paper presented at Conference 2020 American Control Conference (ACC), 1-3 July 2020, Sheraton Denver Downtown Hotel, Denver, CO, USA.
Open this publication in new window or tab >>Toward Tractable Global Solutions to Bayesian Point Estimation Problems via Sparse Sum-of-Squares Relaxations
2020 (English)In: The 2020 American Control Conference, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Bayesian point estimation is commonly used for system identification owing to its good properties for small sample sizes. Although this type of estimator is usually non-parametric, Bayes estimates can also be obtained for rational parametric models, which is often of interest. However, as in maximum-likelihood methods, the Bayes estimate is typically computed via local numerical optimization that requires good initialization and cannot guarantee global optimality. In this contribution, we propose a computationally tractable method that computes the Bayesian parameter estimates with posterior certification of global optimality via sum-of-squares polynomials and sparse semidefinite relaxations. It is shown that the method is applicable to certain discrete-time linear models, which takes advantage of the rational structure of these models and the sparsity in the Bayesian parameter estimation problem. The method is illustrated on a simulation model of a resonant system that is difficult to handle when the sample size is small.

Keywords
Bayesian Identification; Regularization; LMIs; non-convex; relaxation; sum-of-squares; SOS; sparse
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-266754 (URN)
Conference
Conference 2020 American Control Conference (ACC), 1-3 July 2020, Sheraton Denver Downtown Hotel, Denver, CO, USA
Funder
Vinnova, 2016-05181Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

QC 20200120

Available from: 2020-01-17 Created: 2020-01-17 Last updated: 2020-02-03Bibliographically approved
Wang, M., Jacobsen, E. W., Chotteau, V. & Hjalmarsson, H. (2019). A multi-step least-squares method for nonlinear rational models. In: Proceedings of the American Control Conference: . Paper presented at 2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019 (pp. 4509-4514). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8814404.
Open this publication in new window or tab >>A multi-step least-squares method for nonlinear rational models
2019 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4509-4514, article id 8814404Conference paper, Published paper (Refereed)
Abstract [en]

Models rational in the parameters arise frequently in biosystems and other applications. As with all models that are non-linear in the parameters, direct parameter estimation, using e.g. nonlinear least-squares, can become challenging due to the issues of local minima and finding good initial estimates. Here we propose a multi-step least-squares method for a class of nonlinear rational models. The proposed method is applied to an extended Monod-type model. Numerical simulations indicate that the proposed method is consistent.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-262599 (URN)2-s2.0-85072268828 (Scopus ID)9781538679265 (ISBN)
Conference
2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019
Note

QC 20191028

Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28Bibliographically approved
Risuleo, R. S., Lindsten, F. & Hjalmarsson, H. (2019). Bayesian nonparametric identification of Wiener systems. Automatica, 108, Article ID UNSP 108480.
Open this publication in new window or tab >>Bayesian nonparametric identification of Wiener systems
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 108, article id UNSP 108480Article in journal (Refereed) Published
Abstract [en]

We propose a nonparametric approach for the identification of Wiener systems. We model the impulse response of the linear block and the static nonlinearity using Gaussian processes. The hyperparameters of the Gaussian processes are estimated using an iterative algorithm based on stochastic approximation expectation-maximization. In the iterations, we use elliptical slice sampling to approximate the posterior distribution of the impulse response and update the hyperparameter estimates. The same sampling is finally used to sample the posterior distribution and to compute point estimates. We compare the proposed approach with a parametric approach and a semi-parametric approach. In particular, we show that the proposed method has an advantage when a parametric model for the system is not readily available.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Spline models, Nonparametric estimation, System identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-260160 (URN)10.1016/j.automatica.2019.06.032 (DOI)000483631400016 ()2-s2.0-85068777049 (Scopus ID)
Note

QC 20191001

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2019-10-01Bibliographically approved
Galrinho, M., Rojas, C. R. & Hjalmarsson, H. (2019). Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting. Automatica, 102, 45-57
Open this publication in new window or tab >>Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 102, p. 45-57Article in journal (Refereed) Published
Abstract [en]

Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) increases the model complexity, which can cause additional numerical problems for PEM. In this paper, we consider the weighted null-space fitting (WNSF) method. With this method, the system is first modeled using a non-parametric ARX model, which is then reduced to a parametric model of interest using weighted least squares. In the reduction step, a parametric noise model does not need to be estimated if it is not of interest. Because the flexibility of the noise model is increased with the sample size, this will still provide consistent estimates in closed loop and asymptotically efficient estimates in open loop. In this paper, we prove these results, and we derive the asymptotic covariance for the estimation error obtained in closed loop, which is optimal for an infinite-order noise model. For this purpose, we also derive a new technical result for geometric variance analysis, instrumental to our end. Finally, we perform a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Closed-loop identification, Identification algorithms, Least squares, Non-parametric identification, Parameter identification, System identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-246463 (URN)10.1016/j.automatica.2018.12.039 (DOI)000461725600006 ()2-s2.0-85060237267 (Scopus ID)
Note

QC 20190326

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-09Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2019). Linear Prediction Error Methods for Stochastic Nonlinear Models. Automatica, 105, 49-63
Open this publication in new window or tab >>Linear Prediction Error Methods for Stochastic Nonlinear Models
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed) Published
Abstract [en]

The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Parameter estimation; System identification; Stochastic systems; Nonlinear models; Prediction error methods.
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-235340 (URN)10.1016/j.automatica.2019.03.006 (DOI)000476963500005 ()2-s2.0-85063614946 (Scopus ID)
Funder
Swedish Research Council, 2015-05285 : 2016-06079
Note

QC 20180921

Available from: 2018-09-21 Created: 2018-09-21 Last updated: 2020-01-31Bibliographically approved
Risuleo, R. S., Bottegal, G. & Hjalmarsson, H. (2019). Modeling and identification of uncertain-input systems. Automatica, 105, 130-141
Open this publication in new window or tab >>Modeling and identification of uncertain-input systems
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 130-141Article in journal (Refereed) Published
Abstract [en]

We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Estimation algorithms, Gaussian processes, Nonlinear models, Nonparametric identification, System identification, Gaussian noise (electronic), Identification (control systems), Iterative methods, Linear systems, Religious buildings, Blind system identification, Empirical Bayes approach, Estimation algorithm, Non-linear model, Non-parametric identification, Posterior distributions, System identification problems, Gaussian distribution
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-252499 (URN)10.1016/j.automatica.2019.03.014 (DOI)000476963500013 ()2-s2.0-85063903295 (Scopus ID)
Note

QC 20190711

Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2019-08-12Bibliographically approved
Morelli, F., Bombois, X., Hjalmarsson, H., Bako, L. & Colin, K. (2019). Optimal Experiment Design for the Identification of One Module in the Interconnection of Locally Controlled Systems. In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC): . Paper presented at 18th European Control Conference (ECC), Naples, ITALY, JUN 25-28, 2019 (pp. 363-368). IEEE
Open this publication in new window or tab >>Optimal Experiment Design for the Identification of One Module in the Interconnection of Locally Controlled Systems
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2019 (English)In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2019, p. 363-368Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider the problem of designing the least costly experiment that leads to a sufficiently accurate estimate of one module in a network of locally controlled systems. A module in such a network can be identified by exciting the corresponding local closed loop system. Such an excitation signal will not only perturb the input/output of the to-be-identified module, but also other modules due to the interconnection. Consequently, the cost of the identification can be expressed as the sum of the influence of the excitation signal on the inputs and outputs of all locally controlled systems. We develop a methodology to design the spectrum of the excitation signal in such a way that this cost is minimized while guaranteeing a certain accuracy for the identified model. We also propose an alternative identification configuration which can further reduce the propagation of the excitation signal to other modules and we make steps to robustify this optimal experiment design problem with respect to the cost of the identification.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-263407 (URN)10.23919/ECC.2019.8795694 (DOI)000490488300059 ()2-s2.0-85071533287 (Scopus ID)
Conference
18th European Control Conference (ECC), Naples, ITALY, JUN 25-28, 2019
Note

QC 20191107

Available from: 2019-11-07 Created: 2019-11-07 Last updated: 2019-11-07Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

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