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Becker L, Sturm J, Eiden F, Holtmann D. Analyzing and understanding the robustness of bioprocesses. Trends Biotechnol 2023; 41:1013-1026. [PMID: 36959084 DOI: 10.1016/j.tibtech.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/25/2023]
Abstract
The robustness of bioprocesses is becoming increasingly important. The main driving forces of this development are, in particular, increasing demands on product purities as well as economic aspects. In general, bioprocesses exhibit extremely high complexity and variability. Biological systems often have a much higher intrinsic variability compared with chemical processes, which makes the development and characterization of robust processes tedious task. To predict and control robustness, a clear understanding of interactions between input and output variables is necessary. Robust bioprocesses can be realized, for example, by using advanced control strategies for the different unit operations. In this review, we discuss the different biological, technical, and mathematical tools for the analysis and control of bioprocess robustness.
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Affiliation(s)
- Lucas Becker
- Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen, Wiesenstrasse 14, 35390 Giessen, Germany
| | - Jonathan Sturm
- Bioprozesstechnik Group, Westfälische Hochschule, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany; iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Frank Eiden
- Bioprozesstechnik Group, Westfälische Hochschule, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Dirk Holtmann
- Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen, Wiesenstrasse 14, 35390 Giessen, Germany.
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2
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Irreversible and reversible impact on cellular behavior upon intra-experimental process parameter shifts in a CHO semi-continuous perfusion process. Biochem Eng J 2023. [DOI: 10.1016/j.bej.2023.108876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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3
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Bayer B, Duerkop M, Pörtner R, Möller J. Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths. Biotechnol J 2023; 18:e2200381. [PMID: 36382343 DOI: 10.1002/biot.202200381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022]
Abstract
Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.
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Affiliation(s)
| | | | - Ralf Pörtner
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
| | - Johannes Möller
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
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4
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Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9090438. [PMID: 36134983 PMCID: PMC9495725 DOI: 10.3390/bioengineering9090438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/15/2022] [Accepted: 09/02/2022] [Indexed: 11/23/2022]
Abstract
Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.
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Bayer B, Duerkop M, Striedner G, Sissolak B. Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments. Front Bioeng Biotechnol 2022; 9:740215. [PMID: 35004635 PMCID: PMC8733703 DOI: 10.3389/fbioe.2021.740215] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.
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Affiliation(s)
- Benjamin Bayer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Novasign GmbH, Vienna, Austria
| | - Mark Duerkop
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Novasign GmbH, Vienna, Austria
| | - Gerald Striedner
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Novasign GmbH, Vienna, Austria
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6
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Kager J, Herwig C. Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses. Bioengineering (Basel) 2021; 8:160. [PMID: 34821726 PMCID: PMC8614739 DOI: 10.3390/bioengineering8110160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/14/2021] [Accepted: 10/19/2021] [Indexed: 11/29/2022] Open
Abstract
During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.
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Affiliation(s)
- Julian Kager
- Competence Center CHASE GmbH, 4040 Linz, Austria
- Research Division Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, 1060 Vienna, Austria
| | - Christoph Herwig
- Research Division Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, 1060 Vienna, Austria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
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Smiatek J, Clemens C, Herrera LM, Arnold S, Knapp B, Presser B, Jung A, Wucherpfennig T, Bluhmki E. Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes. BIOTECHNOLOGY REPORTS (AMSTERDAM, NETHERLANDS) 2021; 31:e00640. [PMID: 34159058 PMCID: PMC8193373 DOI: 10.1016/j.btre.2021.e00640] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/24/2021] [Accepted: 05/27/2021] [Indexed: 01/02/2023]
Abstract
The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.
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Affiliation(s)
- Jens Smiatek
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
- Boehringer Ingelheim Pharma GmbH & Co. KG, Digitalization Development Biologicals CMC, D-88397 Biberach (Riss), Germany
| | - Christoph Clemens
- Boehringer Ingelheim Pharma GmbH & Co. KG, Focused Factory Drug Substance, D-88397 Biberach (Riss), Germany
| | - Liliana Montano Herrera
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Sabine Arnold
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Bettina Knapp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Beate Presser
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Alexander Jung
- Boehringer Ingelheim Pharma GmbH & Co. KG, Digitalization Development Biologicals CMC, D-88397 Biberach (Riss), Germany
| | - Thomas Wucherpfennig
- Boehringer Ingelheim Pharma GmbH & Co. KG, Bioprocess Development Biologicals, D-88397 Biberach (Riss), Germany
| | - Erich Bluhmki
- Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany
- University of Applied Sciences Biberach, D-88397 Biberach (Riss), Germany
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8
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Novel Strategy for the Calorimetry-Based Control of Fed-Batch Cultivations of Saccharomyces cerevisiae. Processes (Basel) 2021. [DOI: 10.3390/pr9040723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Typical controllers for fed-batch cultivations are based on the estimation and control of the specific growth rate in real time. Biocalorimetry allows one to measure a heat signal proportional to the substrate consumed by cells. The derivative of this heat signal is usually used to evaluate the specific growth rate, introducing noise to the resulting estimate. To avoid this, this study investigated a novel controller based directly on the heat signal. Time trajectories of the heat signal setpoint were modelled for different specific growth rates, and the controller was set to follow this dynamic setpoint. The developed controller successfully followed the setpoint during aerobic cultivations of Saccharomyces cerevisiae, preventing the Crabtree effect by maintaining low glucose concentrations. With this new method, fed-batch cultivations of S. cerevisiae could be reliably controlled at specific growth rates between 0.075 h−1 and 0.20 h−1, with average root mean square errors of 15 ± 3%.
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9
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Széliová D, Iurashev D, Ruckerbauer DE, Koellensperger G, Borth N, Melcher M, Zanghellini J. Error propagation in constraint-based modeling of Chinese hamster ovary cells. Biotechnol J 2021; 16:e2000320. [PMID: 33340257 DOI: 10.1002/biot.202000320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/11/2020] [Indexed: 11/08/2022]
Abstract
Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational system-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet, it is not standard practice to devote sufficient attention to the accurate determination of these rates. In this work, we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analyzed time points. We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.
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Affiliation(s)
- Diana Széliová
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,acib - Austrian Centre of Industrial Biotechnology, Vienna, Austria.,Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - Dmytro Iurashev
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,acib - Austrian Centre of Industrial Biotechnology, Vienna, Austria.,Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | - David E Ruckerbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,acib - Austrian Centre of Industrial Biotechnology, Vienna, Austria.,Department of Analytical Chemistry, University of Vienna, Vienna, Austria
| | | | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,acib - Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Michael Melcher
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Jürgen Zanghellini
- acib - Austrian Centre of Industrial Biotechnology, Vienna, Austria.,Department of Analytical Chemistry, University of Vienna, Vienna, Austria
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Richelle A, Lee BW, Portela RMC, Raley J, Stosch M. Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation. Biotechnol J 2020; 15:e2000113. [DOI: 10.1002/biot.202000113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/30/2020] [Indexed: 12/19/2022]
Affiliation(s)
- Anne Richelle
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
| | - Boung Wook Lee
- Microbial and Cell Culture Development Biopharm Product Development & Supply, GSK King of Prussia PA 19406 USA
| | - Rui M. C. Portela
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
| | - Jonathan Raley
- Microbial and Cell Culture Development Biopharm Product Development & Supply, GSK King of Prussia PA 19406 USA
| | - Moritz Stosch
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
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Bayer B, Striedner G, Duerkop M. Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization. Biotechnol J 2020; 15:e2000121. [DOI: 10.1002/biot.202000121] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/11/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Benjamin Bayer
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Gerald Striedner
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Mark Duerkop
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
- Novasign GmbH Vienna 1190 Austria
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12
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Bayer B, Stosch M, Striedner G, Duerkop M. Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization. Biotechnol J 2020; 15:e1900551. [DOI: 10.1002/biot.201900551] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/28/2020] [Indexed: 12/16/2022]
Affiliation(s)
- Benjamin Bayer
- Department of BiotechnologyUniversity of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Moritz Stosch
- School of Chemical Engineering and Advanced MaterialsNewcastle University Newcastle upon Tyne NE1 7RU UK
| | - Gerald Striedner
- Department of BiotechnologyUniversity of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Mark Duerkop
- Department of BiotechnologyUniversity of Natural Resources and Life Sciences Vienna 1190 Austria
- Novasign GmbH Vienna 1190 Austria
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Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis. Microorganisms 2019; 7:microorganisms7120620. [PMID: 31783658 PMCID: PMC6955888 DOI: 10.3390/microorganisms7120620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/14/2019] [Accepted: 11/20/2019] [Indexed: 12/22/2022] Open
Abstract
Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.
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