Integrating data-driven strategies into model predictive control for enhanced production of human interferon α2b in glycoengineered Pichia pastoris

Integrating data-driven strategies into model predictive control for enhanced production of human interferon α2b in glycoengineered Pichia pastoris | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating data-driven strategies into model predictive control for enhanced production of human interferon α2b in glycoengineered Pichia pastoris Satya Sai Pavan Allampalli, Shikha Solanki, Senthilkumar Sivaprakasam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6707781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The biopharmaceutical industry is witnessing exponential growth and continuously seeking for a scalable optimisation and control strategy to meet the process constraints and objectives. Model predictive control (MPC) has emerged as a robust approach to realise enhanced control over process parameters in bioprocesses. The effectiveness of MPC hinges on the availability of a robust and workable process model. Although mechanistic models are preferred, practical constraints may limit their feasibility and researchers have resorted to data-driven approaches. In this work, we demonstrate the applicability of two data-driven models namely Artificial Neural Networks (ANN) and Gaussian Process (GP) in MPC applications in fed-batch cultivation of glycoengineered P. pastoris for the production of human interferon α2b (huIFN α2b). The experimental verification was carried out by performing fed-batch cultivation in a fermentation calorimeter. Real time monitoring of P. pastoris metabolism was facilitated by metabolic heat rate, capacitance, and exhaust gas analyser measurements. GP-based MPC demonstrated better control, efficient utilization of substrate and 1.1-fold enhanced huIFNα2b productivity compared to ANN-based MPC. Moreover, optimal feeding adapted by GP-based MPC resulted in a 14% decrease in methanol utilization compared with ANN-based MPC. Model predictive control Data-driven P. pastoris ANN Gaussian process Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6707781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466250364,"identity":"bff9b413-7b7c-4df1-a131-d1d3a7bc13ab","order_by":0,"name":"Satya Sai Pavan Allampalli","email":"","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":false,"prefix":"","firstName":"Satya","middleName":"Sai Pavan","lastName":"Allampalli","suffix":""},{"id":466250365,"identity":"fd5fc7c0-6258-4572-8c89-0994590a1eae","order_by":1,"name":"Shikha Solanki","email":"","orcid":"","institution":"Indian Institute of Technology Guwahati","correspondingAuthor":false,"prefix":"","firstName":"Shikha","middleName":"","lastName":"Solanki","suffix":""},{"id":466250366,"identity":"94e90f71-b47d-467c-99ff-aa68efe9787b","order_by":2,"name":"Senthilkumar Sivaprakasam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYJACZgYDCwMw64OBBFTMAK8OxmYGAwmwEsYZBhISRGphgGhh5gGyCDpKvr33+OOCAgljg2uHn322KbCok3dgfviBoeAOTi0GZ84lNgPdY2ZwO814dg7QYYYH2IwlGAye4dYikWPYzGMgYWNwO8GYGaylgcEMKH4Yt8Pmv4FpSf/MbAHWwv4NrxaGGzxgLUCH5RgDQ1tCQp6BB78tBmdyDGcDtRhL3s4pZuwxkJDcwMxTLJGAz2HtZww+8/yxMey7nb6Z4cefOn759vaNHz78weMwTHtBihNI0AC0t4Ek5aNgFIyCUTACAADqPke1uOoF0gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6064-9061","institution":"Indian Institute of Technology Guwahati Department of Biosciences and Bioengineering","correspondingAuthor":true,"prefix":"","firstName":"Senthilkumar","middleName":"","lastName":"Sivaprakasam","suffix":""}],"badges":[],"createdAt":"2025-05-20 12:29:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6707781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6707781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84062978,"identity":"dfcfe181-58a3-443f-99a3-b6ffb46e3adf","added_by":"auto","created_at":"2025-06-06 10:38:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":657906,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6707781/v1_covered_9fb239e0-2e7a-4ba5-ae1f-d2445dcb3641.pdf"}],"financialInterests":"","formattedTitle":"Integrating data-driven strategies into model predictive control for enhanced production of human interferon α2b in glycoengineered Pichia pastoris","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Model predictive control, Data-driven, P. pastoris, ANN, Gaussian process","lastPublishedDoi":"10.21203/rs.3.rs-6707781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6707781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe biopharmaceutical industry is witnessing exponential growth and continuously seeking for a scalable optimisation and control strategy to meet the process constraints and objectives. Model predictive control (MPC) has emerged as a robust approach to realise enhanced control over process parameters in bioprocesses. The effectiveness of MPC hinges on the availability of a robust and workable process model. Although mechanistic models are preferred, practical constraints may limit their feasibility and researchers have resorted to data-driven approaches. In this work, we demonstrate the applicability of two data-driven models namely Artificial Neural Networks (ANN) and Gaussian Process (GP) in MPC applications in fed-batch cultivation of glycoengineered \u003cem\u003eP. pastoris\u003c/em\u003e for the production of human interferon α2b (huIFN α2b). The experimental verification was carried out by performing fed-batch cultivation in a fermentation calorimeter. Real time monitoring of \u003cem\u003eP. pastoris\u003c/em\u003e metabolism was facilitated by metabolic heat rate, capacitance, and exhaust gas analyser measurements. GP-based MPC demonstrated better control, efficient utilization of substrate and 1.1-fold enhanced huIFNα2b productivity compared to ANN-based MPC. Moreover, optimal feeding adapted by GP-based MPC resulted in a 14% decrease in methanol utilization compared with ANN-based MPC.\u003c/p\u003e","manuscriptTitle":"Integrating data-driven strategies into model predictive control for enhanced production of human interferon α2b in glycoengineered Pichia pastoris","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-06 08:22:03","doi":"10.21203/rs.3.rs-6707781/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb9c00bc-1d3d-4d52-9156-5c6093083bd8","owner":[],"postedDate":"June 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-06T10:30:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-06 08:22:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6707781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6707781","identity":"rs-6707781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}