{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86668"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86668","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Optimization Algorithm to Determine Parameters and Track Nonlinear Dynamic Systems in Pharmacology","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Pillai, Nikhil; 0000-0003-3272-0603"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bies, Robert","Computational and Data Enabled Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:36:16Z","date_published":"2025-02-21T21:36:16Z","updated_at":"2026-07-27T19:05:34Z","subjects":["computer science","pharmaceutical sciences"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86668","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bies, Robert","Computational and Data Enabled Sciences"]},{"key":"dc:creator","label":"Author","values":["Pillai, Nikhil; 0000-0003-3272-0603"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:36:16Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science","pharmaceutical sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86668"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. Our research utilizes adaptive chaos synchronization along with grid search and Nelder mead search to estimate physiological and pharmacological systems with nonlinear dynamic characteristics by exploring deterministic methods that are more accurate than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol secretion in human subjects with nonlinear dynamic characteristics and tumor immune interaction model with nonlinear dynamic characteristics. We have also demonstrated the accuracy of adaptive chaos synchronization method in terms of low percent error(/discrepancy) in parameter estimates and low RMSE between predictions and observations by applying this method on both simulated (with and without noise) and on real clinical data in comparison to classical approach used for individual analysis. We also explore deterministic contributors to reproducibility and demonstrate that this method could capture and provide insights into how the actual system gives rise to highly variable results. We observed that this approach can be used for tracking nonlinear dynamic systems more accurately (low RMSE between predictions and observations) compared to classical approaches. Our results demonstrate the strength of this approach for exploring noiseless which only consists of deterministic component and demonstrates the weakness of this approach while analyzing real clinical data which consists of both deterministic and stochastic component.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optimization Algorithm to Determine Parameters and Track Nonlinear Dynamic Systems in Pharmacology"]}]}],"canonical_facts":{"dc:contributor":["Bies, Robert","Computational and Data Enabled Sciences"],"dc:creator":["Pillai, Nikhil; 0000-0003-3272-0603"],"dc:date":["2025-02-21T21:36:16Z","2020"],"dc:description":["Ph.D.","In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. Our research utilizes adaptive chaos synchronization along with grid search and Nelder mead search to estimate physiological and pharmacological systems with nonlinear dynamic characteristics by exploring deterministic methods that are more accurate than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol secretion in human subjects with nonlinear dynamic characteristics and tumor immune interaction model with nonlinear dynamic characteristics. We have also demonstrated the accuracy of adaptive chaos synchronization method in terms of low percent error(/discrepancy) in parameter estimates and low RMSE between predictions and observations by applying this method on both simulated (with and without noise) and on real clinical data in comparison to classical approach used for individual analysis. We also explore deterministic contributors to reproducibility and demonstrate that this method could capture and provide insights into how the actual system gives rise to highly variable results. We observed that this approach can be used for tracking nonlinear dynamic systems more accurately (low RMSE between predictions and observations) compared to classical approaches. Our results demonstrate the strength of this approach for exploring noiseless which only consists of deterministic component and demonstrates the weakness of this approach while analyzing real clinical data which consists of both deterministic and stochastic component.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86668"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science","pharmaceutical sciences"],"dc:title":["Optimization Algorithm to Determine Parameters and Track Nonlinear Dynamic Systems in Pharmacology"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:34Z"}