{"id":{"repo_id":"auckland-ms","oai_identifier":"oai:researchspace.auckland.ac.nz:2292/76143"},"canonical_url":"https://search.dev.ndltd.org/etd/auckland-ms/oai:researchspace.auckland.ac.nz:2292/76143","repository":{"repo_id":"auckland-ms","name":"University of Auckland","base_url":"https://researchspace.auckland.ac.nz/server/oai/request"},"display":{"title":"The Automated Construction and Verification of Physically Plausible Models of Physiological Systems","abstract":"Computational modelling plays a central role in both academic research and clinical applications. As a result, ensuring model reliability has become increasingly important. Since the introduction of advanced computers in the 1960s, the engineering community has tried to increase the credibility of simulations by developing methods for verifying and validating mathematical modelling. However, despite reports on models violating fundamental physical and thermodynamic principles, no systematic approach exists for assessing model credibility and reliability in systems biology. Consequently, modellers in this field have introduced various methods to assess model outputs by comparing simulation results with experimental data. Additionally, some approaches constrain the probable states of a model through the evaluation of energy flux, thereby eliminating implausible states. Nevertheless, there is no comprehensive framework capable of evaluating a model's overall physical and thermodynamic plausibility. To address this, I developed a novel method to assess and verify systems biology models and demonstrated the utility of this method by implementing it in a Python tool. This tool implements checks for mass, charge, and energy conservation. It automatically reads CellML and SBML models and evaluates their consistency with physical and thermodynamic principles. When applied to the BioModels Database, my tool identified 182 models governed by mass-action kinetics. Among these, 22 were found to exhibit reversible reactions, and 13 complied with physical and thermodynamic principles, making them plausible. These results underscore the need for attention to physical and thermodynamic consistency in simulations of systems biology models. They also highlight that agreement with experimental observations is not sufficient for robust and reliable models.","abstract_html":"Computational modelling plays a central role in both academic research and clinical applications. As a result, ensuring model reliability has become increasingly important. Since the introduction of advanced computers in the 1960s, the engineering community has tried to increase the credibility of simulations by developing methods for verifying and validating mathematical modelling. However, despite reports on models violating fundamental physical and thermodynamic principles, no systematic approach exists for assessing model credibility and reliability in systems biology. Consequently, modellers in this field have introduced various methods to assess model outputs by comparing simulation results with experimental data. Additionally, some approaches constrain the probable states of a model through the evaluation of energy flux, thereby eliminating implausible states. Nevertheless, there is no comprehensive framework capable of evaluating a model's overall physical and thermodynamic plausibility. To address this, I developed a novel method to assess and verify systems biology models and demonstrated the utility of this method by implementing it in a Python tool. This tool implements checks for mass, charge, and energy conservation. It automatically reads CellML and SBML models and evaluates their consistency with physical and thermodynamic principles. When applied to the BioModels Database, my tool identified 182 models governed by mass-action kinetics. Among these, 22 were found to exhibit reversible reactions, and 13 complied with physical and thermodynamic principles, making them plausible. These results underscore the need for attention to physical and thermodynamic consistency in simulations of systems biology models. They also highlight that agreement with experimental observations is not sufficient for robust and reliable models.","abstract_has_math":false,"creators":["Akbarpour Ghazani, Mehran"],"institution":"ResearchSpace@Auckland","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":[],"advisors":["Pan, Michael","Tran, Kenneth","Nickerson, David P"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T01:04:52Z","subjects":["Verification and Validation","Thermodynamic Consistency","CellML","SBML"],"languages":[],"rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"rights_urls":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2292/76143","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Pan, Michael","Tran, Kenneth","Nickerson, David P"]},{"key":"dc:creator","label":"Author","values":["Akbarpour Ghazani, Mehran"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-28T21:27:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["ResearchSpace@Auckland"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PhD"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Auckland"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Verification and Validation","Thermodynamic Consistency","CellML","SBML"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2292/76143"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Computational modelling plays a central role in both academic research and clinical applications. As a result, ensuring model reliability has become increasingly important. Since the introduction of advanced computers in the 1960s, the engineering community has tried to increase the credibility of simulations by developing methods for verifying and validating mathematical modelling. However, despite reports on models violating fundamental physical and thermodynamic principles, no systematic approach exists for assessing model credibility and reliability in systems biology. Consequently, modellers in this field have introduced various methods to assess model outputs by comparing simulation results with experimental data. Additionally, some approaches constrain the probable states of a model through the evaluation of energy flux, thereby eliminating implausible states. Nevertheless, there is no comprehensive framework capable of evaluating a model's overall physical and thermodynamic plausibility. To address this, I developed a novel method to assess and verify systems biology models and demonstrated the utility of this method by implementing it in a Python tool. This tool implements checks for mass, charge, and energy conservation. It automatically reads CellML and SBML models and evaluates their consistency with physical and thermodynamic principles. When applied to the BioModels Database, my tool identified 182 models governed by mass-action kinetics. Among these, 22 were found to exhibit reversible reactions, and 13 complied with physical and thermodynamic principles, making them plausible. These results underscore the need for attention to physical and thermodynamic consistency in simulations of systems biology models. They also highlight that agreement with experimental observations is not sufficient for robust and reliable models."]},{"key":"dc:title","label":"Title","values":["The Automated Construction and Verification of Physically Plausible Models of Physiological Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pan, Michael","Tran, Kenneth","Nickerson, David P"],"dc:creator":["Akbarpour Ghazani, Mehran"],"dc:date.accessioned":["2026-06-28T21:27:34Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Computational modelling plays a central role in both academic research and clinical applications. As a result, ensuring model reliability has become increasingly important. Since the introduction of advanced computers in the 1960s, the engineering community has tried to increase the credibility of simulations by developing methods for verifying and validating mathematical modelling. However, despite reports on models violating fundamental physical and thermodynamic principles, no systematic approach exists for assessing model credibility and reliability in systems biology. Consequently, modellers in this field have introduced various methods to assess model outputs by comparing simulation results with experimental data. Additionally, some approaches constrain the probable states of a model through the evaluation of energy flux, thereby eliminating implausible states. Nevertheless, there is no comprehensive framework capable of evaluating a model's overall physical and thermodynamic plausibility. To address this, I developed a novel method to assess and verify systems biology models and demonstrated the utility of this method by implementing it in a Python tool. This tool implements checks for mass, charge, and energy conservation. It automatically reads CellML and SBML models and evaluates their consistency with physical and thermodynamic principles. When applied to the BioModels Database, my tool identified 182 models governed by mass-action kinetics. Among these, 22 were found to exhibit reversible reactions, and 13 complied with physical and thermodynamic principles, making them plausible. These results underscore the need for attention to physical and thermodynamic consistency in simulations of systems biology models. They also highlight that agreement with experimental observations is not sufficient for robust and reliable models."],"dc:identifier.uri":["https://hdl.handle.net/2292/76143"],"dc:publisher":["ResearchSpace@Auckland"],"dc:rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"dc:rights.uri":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"dc:subject":["Verification and Validation","Thermodynamic Consistency","CellML","SBML"],"dc:title":["The Automated Construction and Verification of Physically Plausible Models of Physiological Systems"],"dc:type":["Thesis"],"thesis:degree_discipline":["Bioengineering"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["PhD"],"thesis:institution_name":["The University of Auckland"]},"updated_at":"2026-07-24T01:04:52Z"}