Machine Learning Investigation of Electroporation of Cell Clusters in Cancer Therapy Using Adaptive Neuro-Fuzzy Inference and Finite Element Modeling

Machine Learning Investigation of Electroporation of Cell Clusters in Cancer Therapy Using Adaptive Neuro-Fuzzy Inference and Finite Element Modeling | 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 Machine Learning Investigation of Electroporation of Cell Clusters in Cancer Therapy Using Adaptive Neuro-Fuzzy Inference and Finite Element Modeling Salim Mirshahi, Ameneh Sazgarnia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6048726/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 Exposing biological cells to an external electric field results in an induced transmembrane voltage (ITV) on the cell membrane, which, under the right conditions may result in reversible permeabilization. This process, called electroporation, has been used to deliver drugs during cancer therapy, DNA transfection or transformation, direct transfer of plasmids between cells and gene therapy. Given the broad importance of these applications, fundamental understanding and optimization of electroporation is critically needed. Towards this goal, this work utilizes machine learning techniques to study the influence of cell-cell interactions on ITV during electropermeabilization. In the first part of the study, the ITV of a cell positioned at the center of a cell cluster is studied. An adaptive neuro-fuzzy inference system (ANFIS) model combined with finite-element simulations is used to develop a tool to predict the electroporated cell surface area (ECSA) without the need for cumbersome measurements or time-intensive simulations. The finite-element simulations were used to compute the electric potential distribution in the media during electroporation of a single cell or cluster of cells. This approach is shown to be able to accurately predict the ECSA during electroporation. Results indicate that the packing ratio (PR) of the cell cluster plays a key role in determining the ITV, particularly when the PR is less than 3. The sensitivity is much reduced for larger values of PR. The ANFIS model showed a 96.6% coefficient of determination and 1.46 root mean square error, showing that the ANFIS model is a reasonably accurate and robust approach for prediction of ECSA. A key result from this work is that a higher amplitude of AEF is needed to have higher ECSA when PR decreases. Insights gained from this work may contribute towards investigating other types of cancer with various cell densities with the aim of determining optimal electric field parameters. Electroporation Adaptive Neuro-Fuzzy Inference System Cell Cluster Finite Element Modeling Induced Transmembrane Voltages Cancer Cell Viability Full Text Additional Declarations The authors declare no competing interests. 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-6048726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416968394,"identity":"403811fa-2961-44e9-8607-fa7a80988ba8","order_by":0,"name":"Salim Mirshahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACxgYQSmBjYD744AMDQwIpWtiSDWcQpwWiC6iSx0yahxgt8tMOt338ucMuj4+BLU3ats0uj5+9gfHDxxzcWgxuJzbP5j2TXAz0y2Hr3LbkYsmeA8ySM7fh0SKd2MzM2Mac2MbAlng7F8jYcCOBjZkXjxb52YnNjD/b6oFaeAykLYEMgloYgA5j4G07DNJiJM0IZBDUAvILM2/bcZDDkg17zh1PnNlzsBmvX+Rnpz8GOqw6cX4DMCp/lFUn9rM3H/zwEZ/DEJofACOIDcQCRhMJ4A8pikfBKBgFo2CkAADDT1JhqHrBSwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Salim","middleName":"","lastName":"Mirshahi","suffix":""},{"id":416968472,"identity":"6731cc4d-e1b5-41f9-9b12-ebba4f919617","order_by":1,"name":"Ameneh Sazgarnia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ameneh","middleName":"","lastName":"Sazgarnia","suffix":""}],"badges":[],"createdAt":"2025-02-17 14:11:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6048726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6048726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76629199,"identity":"a95645fe-10d8-4310-a62a-b7d0726c5c99","added_by":"auto","created_at":"2025-02-19 06:21:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":943496,"visible":true,"origin":"","legend":"","description":"","filename":"TheoreticalModeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6048726/v1_covered_ce80ec7e-07ac-43fe-b4ea-38d89994bd40.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine Learning Investigation of Electroporation of Cell Clusters in Cancer Therapy Using Adaptive Neuro-Fuzzy Inference and Finite Element Modeling\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mashhad University of Medical Sciences","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":"Electroporation, Adaptive Neuro-Fuzzy Inference System, Cell Cluster, Finite Element Modeling, Induced Transmembrane Voltages, Cancer Cell Viability","lastPublishedDoi":"10.21203/rs.3.rs-6048726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6048726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExposing biological cells to an external electric field results in an induced transmembrane voltage (ITV) on the cell membrane, which, under the right conditions may result in reversible permeabilization. This process, called electroporation, has been used to deliver drugs during cancer therapy, DNA transfection or transformation, direct transfer of plasmids between cells and gene therapy. Given the broad importance of these applications, fundamental understanding and optimization of electroporation is critically needed. Towards this goal, this work utilizes machine learning techniques to study the influence of cell-cell interactions on ITV during electropermeabilization.\u003cstrong\u003e \u003c/strong\u003eIn the first part of the study, the ITV of a cell positioned at the center of a cell cluster is studied. An adaptive neuro-fuzzy inference system (ANFIS) model combined with finite-element simulations is used to develop a tool to predict the electroporated cell surface area (ECSA) without the need for cumbersome measurements or time-intensive simulations. The finite-element simulations were used to compute the electric potential distribution in the media during electroporation of a single cell or cluster of cells. This approach is shown to be able to accurately predict the ECSA during electroporation. Results indicate that the packing ratio (PR) of the cell cluster plays a key role in determining the ITV, particularly when the PR is less than 3. The sensitivity is much reduced for larger values of PR. The ANFIS model showed a 96.6% coefficient of determination and 1.46 root mean square error, showing that the ANFIS model is a reasonably accurate and robust approach for prediction of ECSA. A key result from this work is that a higher amplitude of AEF is needed to have higher ECSA when PR decreases. 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