Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors
•Mechanistic models developed to describe bioprocesses inside microbioreactors.•Models allow fast identification of reaction mechanism, kinetics and limitations.•Fluid flow and enzyme adsorption affects response of optical sensor inside μBR.•Extra catalase and hydrogen peroxide in μBR disturb local...
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creator | Semenova, Daria Fernandes, Ana C. Bolivar, Juan M. Rosinha Grundtvig, Inês P. Vadot, Barbara Galvanin, Silvia Mayr, Torsten Nidetzky, Bernd Zubov, Alexandr Gernaey, Krist V. |
description | •Mechanistic models developed to describe bioprocesses inside microbioreactors.•Models allow fast identification of reaction mechanism, kinetics and limitations.•Fluid flow and enzyme adsorption affects response of optical sensor inside μBR.•Extra catalase and hydrogen peroxide in μBR disturb local oxygen concentrations.•Framework created screening bioprocesses, sensor response and μBR designs.
Design and development of scale-down approaches, such as microbioreactor (μBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of μBRs in biotechnology remains the lack of appropriate software and automated data interpretation of μBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for μL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of μBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs. |
doi_str_mv | 10.1016/j.nbt.2019.11.001 |
format | Article |
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Design and development of scale-down approaches, such as microbioreactor (μBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of μBRs in biotechnology remains the lack of appropriate software and automated data interpretation of μBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for μL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of μBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.</description><identifier>ISSN: 1871-6784</identifier><identifier>EISSN: 1876-4347</identifier><identifier>DOI: 10.1016/j.nbt.2019.11.001</identifier><identifier>PMID: 31704414</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Bioprocess modeling ; Bioreactors ; Biotechnology ; Cascade chemical reactions ; Catalase ; Catalysis ; Chemical reduction ; Computational fluid dynamics ; Computer applications ; Computer simulation ; Data interpretation ; Enzymatic biocatalysis ; Fluid dynamics ; Fluid flow ; Glucose oxidase ; Hydrodynamics ; Limiting factors ; Mathematical models ; Mechanistic modeling ; Microbioreactor ; Microfluidics ; Monitoring ; Optical measuring instruments ; Oxygen monitoring ; Oxygen probes ; Reaction kinetics ; Reaction mechanisms ; Reagents ; Screening ; Sensors</subject><ispartof>New biotechnology, 2020-05, Vol.56, p.27-37</ispartof><rights>2019 Elsevier B.V.</rights><rights>Copyright © 2019 Elsevier B.V. All rights reserved.</rights><rights>Copyright Elsevier Science Ltd. May 25, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-c64e70b45c426a0a74c432f37aac555419cd235c3de8a4f4e6a134d375ac887b3</citedby><cites>FETCH-LOGICAL-c424t-c64e70b45c426a0a74c432f37aac555419cd235c3de8a4f4e6a134d375ac887b3</cites><orcidid>0000-0001-6132-8112 ; 0000-0002-9204-0979 ; 0000-0002-0364-1773 ; 0000-0003-2494-9934</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.nbt.2019.11.001$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31704414$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Semenova, Daria</creatorcontrib><creatorcontrib>Fernandes, Ana C.</creatorcontrib><creatorcontrib>Bolivar, Juan M.</creatorcontrib><creatorcontrib>Rosinha Grundtvig, Inês P.</creatorcontrib><creatorcontrib>Vadot, Barbara</creatorcontrib><creatorcontrib>Galvanin, Silvia</creatorcontrib><creatorcontrib>Mayr, Torsten</creatorcontrib><creatorcontrib>Nidetzky, Bernd</creatorcontrib><creatorcontrib>Zubov, Alexandr</creatorcontrib><creatorcontrib>Gernaey, Krist V.</creatorcontrib><title>Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors</title><title>New biotechnology</title><addtitle>N Biotechnol</addtitle><description>•Mechanistic models developed to describe bioprocesses inside microbioreactors.•Models allow fast identification of reaction mechanism, kinetics and limitations.•Fluid flow and enzyme adsorption affects response of optical sensor inside μBR.•Extra catalase and hydrogen peroxide in μBR disturb local oxygen concentrations.•Framework created screening bioprocesses, sensor response and μBR designs.
Design and development of scale-down approaches, such as microbioreactor (μBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of μBRs in biotechnology remains the lack of appropriate software and automated data interpretation of μBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for μL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of μBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.</description><subject>Bioprocess modeling</subject><subject>Bioreactors</subject><subject>Biotechnology</subject><subject>Cascade chemical reactions</subject><subject>Catalase</subject><subject>Catalysis</subject><subject>Chemical reduction</subject><subject>Computational fluid dynamics</subject><subject>Computer applications</subject><subject>Computer simulation</subject><subject>Data interpretation</subject><subject>Enzymatic biocatalysis</subject><subject>Fluid dynamics</subject><subject>Fluid flow</subject><subject>Glucose oxidase</subject><subject>Hydrodynamics</subject><subject>Limiting factors</subject><subject>Mathematical models</subject><subject>Mechanistic modeling</subject><subject>Microbioreactor</subject><subject>Microfluidics</subject><subject>Monitoring</subject><subject>Optical measuring instruments</subject><subject>Oxygen monitoring</subject><subject>Oxygen probes</subject><subject>Reaction kinetics</subject><subject>Reaction mechanisms</subject><subject>Reagents</subject><subject>Screening</subject><subject>Sensors</subject><issn>1871-6784</issn><issn>1876-4347</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kU-P1DAMxSMEYpeFD8AFVeLCpSVO3KYjTmjFP2kRFzhHaeJCRm0zJBnQfns8zMKBAyfbys9PzntCPAXZgYTh5b7bptopCbsOoJMS7olLGM3QokZz_3cP7WBGvBCPStlLOcBugIfiQoORiICXIn9MgZZ2coVC4za33JZYmjQ3U0zeVZ5r9M0hJ0-lUGEkNAfKc8qr2zydyDX6nBjP5HxNuTQ_Y_3WxK3S1-wqy6YDa7ilKbQVfn8sHsxuKfTkrl6JL2_ffL5-3958evfh-vVN61Fhbf2AZOSEPY-Dk86gR61mbZzzfd8j7HxQuvc60OhwRhocaAza9M6Po5n0lXhx1uXrvx-pVLvG4mlZ3EbpWKzSoPWoeqUYff4Puk_HzG4whShxp2SPTMGZ4v-Wkmm2hxxXl28tSHsKxO4tB2JPgVgAy4HwzrM75eO0Uvi78ScBBl6dAWIrfkTKtvhIbG2ImXy1IcX_yP8CErOc-A</recordid><startdate>20200525</startdate><enddate>20200525</enddate><creator>Semenova, Daria</creator><creator>Fernandes, Ana C.</creator><creator>Bolivar, Juan M.</creator><creator>Rosinha Grundtvig, Inês P.</creator><creator>Vadot, Barbara</creator><creator>Galvanin, Silvia</creator><creator>Mayr, Torsten</creator><creator>Nidetzky, Bernd</creator><creator>Zubov, Alexandr</creator><creator>Gernaey, Krist V.</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6132-8112</orcidid><orcidid>https://orcid.org/0000-0002-9204-0979</orcidid><orcidid>https://orcid.org/0000-0002-0364-1773</orcidid><orcidid>https://orcid.org/0000-0003-2494-9934</orcidid></search><sort><creationdate>20200525</creationdate><title>Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors</title><author>Semenova, Daria ; Fernandes, Ana C. ; Bolivar, Juan M. ; Rosinha Grundtvig, Inês P. ; Vadot, Barbara ; Galvanin, Silvia ; Mayr, Torsten ; Nidetzky, Bernd ; Zubov, Alexandr ; Gernaey, Krist V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-c64e70b45c426a0a74c432f37aac555419cd235c3de8a4f4e6a134d375ac887b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bioprocess modeling</topic><topic>Bioreactors</topic><topic>Biotechnology</topic><topic>Cascade chemical reactions</topic><topic>Catalase</topic><topic>Catalysis</topic><topic>Chemical reduction</topic><topic>Computational fluid dynamics</topic><topic>Computer applications</topic><topic>Computer simulation</topic><topic>Data interpretation</topic><topic>Enzymatic biocatalysis</topic><topic>Fluid dynamics</topic><topic>Fluid flow</topic><topic>Glucose oxidase</topic><topic>Hydrodynamics</topic><topic>Limiting factors</topic><topic>Mathematical models</topic><topic>Mechanistic modeling</topic><topic>Microbioreactor</topic><topic>Microfluidics</topic><topic>Monitoring</topic><topic>Optical measuring instruments</topic><topic>Oxygen monitoring</topic><topic>Oxygen probes</topic><topic>Reaction kinetics</topic><topic>Reaction mechanisms</topic><topic>Reagents</topic><topic>Screening</topic><topic>Sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Semenova, Daria</creatorcontrib><creatorcontrib>Fernandes, Ana C.</creatorcontrib><creatorcontrib>Bolivar, Juan M.</creatorcontrib><creatorcontrib>Rosinha Grundtvig, Inês P.</creatorcontrib><creatorcontrib>Vadot, Barbara</creatorcontrib><creatorcontrib>Galvanin, Silvia</creatorcontrib><creatorcontrib>Mayr, Torsten</creatorcontrib><creatorcontrib>Nidetzky, Bernd</creatorcontrib><creatorcontrib>Zubov, Alexandr</creatorcontrib><creatorcontrib>Gernaey, Krist V.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>New biotechnology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Semenova, Daria</au><au>Fernandes, Ana C.</au><au>Bolivar, Juan M.</au><au>Rosinha Grundtvig, Inês P.</au><au>Vadot, Barbara</au><au>Galvanin, Silvia</au><au>Mayr, Torsten</au><au>Nidetzky, Bernd</au><au>Zubov, Alexandr</au><au>Gernaey, Krist V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors</atitle><jtitle>New biotechnology</jtitle><addtitle>N Biotechnol</addtitle><date>2020-05-25</date><risdate>2020</risdate><volume>56</volume><spage>27</spage><epage>37</epage><pages>27-37</pages><issn>1871-6784</issn><eissn>1876-4347</eissn><abstract>•Mechanistic models developed to describe bioprocesses inside microbioreactors.•Models allow fast identification of reaction mechanism, kinetics and limitations.•Fluid flow and enzyme adsorption affects response of optical sensor inside μBR.•Extra catalase and hydrogen peroxide in μBR disturb local oxygen concentrations.•Framework created screening bioprocesses, sensor response and μBR designs.
Design and development of scale-down approaches, such as microbioreactor (μBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of μBRs in biotechnology remains the lack of appropriate software and automated data interpretation of μBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for μL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of μBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>31704414</pmid><doi>10.1016/j.nbt.2019.11.001</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6132-8112</orcidid><orcidid>https://orcid.org/0000-0002-9204-0979</orcidid><orcidid>https://orcid.org/0000-0002-0364-1773</orcidid><orcidid>https://orcid.org/0000-0003-2494-9934</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bioprocess modeling Bioreactors Biotechnology Cascade chemical reactions Catalase Catalysis Chemical reduction Computational fluid dynamics Computer applications Computer simulation Data interpretation Enzymatic biocatalysis Fluid dynamics Fluid flow Glucose oxidase Hydrodynamics Limiting factors Mathematical models Mechanistic modeling Microbioreactor Microfluidics Monitoring Optical measuring instruments Oxygen monitoring Oxygen probes Reaction kinetics Reaction mechanisms Reagents Screening Sensors |
title | Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors |
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