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Novel column generation-based optimization approach for poly-pathway kinetic model applied to CHO cell culture
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden. (Cell Technology Group)
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Industrial Biotechnology. AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden. (Cell Technology Group)
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0002-6252-7815
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2019 (English)In: Metabolic Engineering Communications, ISSN 2214-0301, Vol. 8, article id e00083Article in journal (Refereed) Published
Abstract [en]

Mathematical modelling can provide precious tools for bioprocess simulation, prediction, control and optimization of mammalian cell-based cultures. In this paper we present a novel method to generate kinetic models of such cultures, rendering complex metabolic networks in a poly-pathway kinetic model. The model is based on subsets of elementary flux modes (EFMs) to generate macro-reactions. Thanks to our column generation-based optimization algorithm, the experimental data are used to identify the EFMs, which are relevant to the data. Here the systematic enumeration of all the EFMs is eliminated and a network including a large number of reactions can be considered. In particular, the poly-pathway model can simulate multiple metabolic behaviors in response to changes in the culture conditions. We apply the method to a network of 126 metabolic reactions describing cultures of antibody-producing Chinese hamster ovary cells, and generate a poly-pathway model that simulates multiple experimental conditions obtained in response to variations in amino acid availability. A good fit between simulated and experimental data is obtained, rendering the variations in the growth, product, and metabolite uptake/secretion rates. The intracellular reaction fluxes simulated by the model are explored, linking variations in metabolic behavior to adaptations of the intracellular metabolism.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 8, article id e00083
Keywords [en]
Amino acid, Chinese hamster ovary cell, Column generation, Elementary flux mode, Kinetic modelling, Metabolic flux analysis, Optimization, Poly-pathway model
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-246415DOI: 10.1016/j.mec.2018.e00083Scopus ID: 2-s2.0-85061356952OAI: oai:DiVA.org:kth-246415DiVA, id: diva2:1301460
Note

QC 20190402

Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-11-19Bibliographically approved
In thesis
1. Macroscopic models of Chinese hamster ovary cell cultures
Open this publication in new window or tab >>Macroscopic models of Chinese hamster ovary cell cultures
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biopharmaceuticals treat a range of diseases, and is a growing sector within the pharmaceutical industry. A majority of these complex molecules are produced by genetically modified mammalian cells in large-scale cell cultures. Biopharmaceutical process development is costly and labor intensive, and has often been based on time-consuming empirical methods and trial-and-error. Mathematical modeling has great potential to speed up this work. A central question however, engaging researchers from various fields, is how to translate these complex biological systems into feasible and useful models.

For biopharmaceutical production, macroscopic kinetic flux modeling has been proposed. This model type is derived from typical data obtained in the industry, and has been able to simulate cell growth and the uptake/secretion of important metabolites. Often, however, their scope is limited to specific culture conditions due to e.g. the lack of information on reaction kinetics, limited data sets, and simplifications to achieve calculability.

In this thesis, the macroscopic kinetic model type is the starting point, but the goal is to capture a variety of culture conditions, as will be necessary for future applications in process optimization. The effects of varied availability of amino acids in the culture medium on cell growth, uptake/secretion of metabolites, and product secretion were studied in cell cultures.

In Paper I, the established methodology of Metatool was tested: (i) a simplified metabolic network of approx. 30 reactions was defined; (ii) all possible so-called elementary flux modes (EFMs) through the network were identified using an established mathematical algorithm; and (iii) the effect on each flux was modelled by a simplified generalized kinetic equation. A limitation was identified; the Metatool algorithm could only handle simple networks, and therefore several reactions had to be discarded. In this paper, a new strategy for the kinetics was developed. A pool of alternative kinetic equations was created, from which a smaller set could be given higher weight as determined via data-fitting. This improved the simulations.

The identification of EFMs was further studied in papers II–IV. In Paper II, a new algorithm was developed based on the column generation optimization technique, that in addition to the network also accounts for the data from one of the parallel cultures. The method identifies a subset of the EFMs that can optimally fit the data, even in more complex metabolic networks.

In Paper III, a kinetic model based on EFM subsets in a 100 reaction network was generated, which further improved the simulations. Finally, in Paper IV, the algorithm was extended to EFM identification in a genome-scale network. Despite the high complexity, small subsets of EFMs relevant to the experimental data could be e ciently identified.

Abstract [sv]

Bioläkemedel används vid behandling av en rad olika sjukdomar och utgör därför en växande sektor inom läkemedelsbranschen. Majoriteten av dessa läkemedel produceras via storskalig cellodling av genetiskt modifierade mammalieceller. Processutvecklingen är dyr och arbetskrävande, och baseras vanligtvis på empirisk erfarenhet och trial-and-error. Matematiska modeller har stor potential för att effektivisera arbetet. En central fråga är dock hur man ska kunna översätta ett så pass komplext biologiskt system till en genomförbar och användbar modell.

För bioläkemedelsproduktion har s. k. makroskopisk kinetisk flödes- modellering föreslagits. Modelltypen bygger på den typ av data som tas fram inom industrin och modellerna har visats kunna simulera celltillväxten, samt cellernas upptag och utsöndring av viktiga metaboliter. Dock är tillämpnings- området ofta begränsat till specifika odlingsvillkor, delvis p.g.a. kunskapsbrist gällande reaktionskinetiken, begränsad tillgång till odlingsdata, samt behovet av beräkningsmässiga förenklingar.

Denna avhandling tar avstamp i makroskopisk kinetisk modellering, men här med målet att fånga upp de mer varierade odlingsvillkor som behövs för att kunna optimera processer. En cellinje studerades först i parallella odlingar med varierad tillgång på aminosyror. Påverkan på tillväxt, upptag/utsöndring av metaboliter och läkemedelsproduktion registrerades.

I artikel I prövades metodiken etablerad i tidigare studier: (i) ett förenklat metaboliskt flödesnätverk om cirka 30–40 reaktioner togs fram; (ii) samtliga s.k. elementära flöden genom nätverket identifierades med en etablerad matematisk algoritm; (iii) påverkan på varje flöde beskrevs av en förenklad och allmän kinetisk ekvation. Dels klarade algoritmen endast mycket förenkla- de nätverk och ett flertal reaktioner kunde därför inte tas med, dels var den kinetiska ekvationen alltför begränsad för att kunna simulera många av flödes- förändringarna i datan. Därför togs en ny strategi för kinetiken fram i artikel I. En pool av alternativa ekvationer skapades, varifrån ett mindre antal kunde ges större vikt via dataanpassning. Detta förbättrade simuleringsresultaten.

Identifieringen av elementära flöden studerades sedan i artiklarna II–IV. I II togs en ny algoritm fram, baserad på en optimeringsteknik kallad kolumngenerering. Algoritmen identifierar en delmängd av de elementära flödena genom ett givet nätverk, med målet att uppnå optimal dataanpassning för en enskild odling. Detta visade sig vara effektivt även för mer komplexa nätverk. I III tillämpades metoden för att simulera samtliga odlingar tillsammans i en enda modell. Den kinetiska modellen kunde nu baserades på en delmängd av flödena i ett stort nätverk om cirka 100 reaktioner, vilket förbättrade simuleringsresultaten ytterligare. I IV, utvidgades till sist den nya algoritmen för identifiering i en genomskalig modell. Trots den höga nivån av komplexitet kunde små delmängder av elementära flöden effektivt tas fram.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 145
Series
TRITA-CBH-FOU ; 2019:45
Keywords
Chinese hamster ovary, Amino acid, Metabolic network, Metabolic flux analysis, Kinetic modeling, Elementary flux mode, Optimization, Column generation
National Category
Industrial Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-262850 (URN)978-91-7873-315-6 (ISBN)
Public defence
2019-11-13, FB54, Albanova universitetscentrum, Roslagstullsbacken 21, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Vinnova
Note

QC 2019-10-22

Available from: 2019-10-22 Created: 2019-10-21 Last updated: 2019-10-22Bibliographically approved

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