{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/125388"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/125388","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"Simulation of lipid membrane models at coarse-grained resolution Towards novel methodologies with improved capabilities","abstract":"This thesis develops computational methodologies for simulating biological membranes at coarse-grained (CG) resolution, combining well-established modeling strategies with recent advances in machine learning. It presents an optimized set of lipid representations and parameters for the Martini 3 CG force field (FF), improving the description of temperature-dependent phase separation in mixtures while also broadening the coverage of lipid types. Building on this foundation, a data-driven optimization protocol based on automatic differentiation is introduced, systematically refining CG FFs by minimizing a multi-objective loss function that combines bottom-up and top-down parametrization targets. To move beyond the intrinsic limitations of such models, a different approach is proposed, using graph neural networks trained on atomistic simulations to learn CG potential energy surfaces. Alongside these model-focused developments, this thesis presents tools to facilitate the analysis and dissemination of CG simulations: i- ProLint2, an efficient Python library for characterizing biomolecular interactions from molecular dynamics trajectories; and ii- a new web portal for the Martini Force Field Initiative, built on an automatically maintained infrastructure and backend operations that support open collaboration in developing and using CG models. Collectively, these contributions advance the methodological framework for simulating biological membranes at CG resolution, with significant applications to membrane biophysics.","abstract_html":"This thesis develops computational methodologies for simulating biological membranes at coarse-grained (CG) resolution, combining well-established modeling strategies with recent advances in machine learning. It presents an optimized set of lipid representations and parameters for the Martini 3 CG force field (FF), improving the description of temperature-dependent phase separation in mixtures while also broadening the coverage of lipid types. Building on this foundation, a data-driven optimization protocol based on automatic differentiation is introduced, systematically refining CG FFs by minimizing a multi-objective loss function that combines bottom-up and top-down parametrization targets. To move beyond the intrinsic limitations of such models, a different approach is proposed, using graph neural networks trained on atomistic simulations to learn CG potential energy surfaces. Alongside these model-focused developments, this thesis presents tools to facilitate the analysis and dissemination of CG simulations: i- ProLint2, an efficient Python library for characterizing biomolecular interactions from molecular dynamics trajectories; and ii- a new web portal for the Martini Force Field Initiative, built on an automatically maintained infrastructure and backend operations that support open collaboration in developing and using CG models. Collectively, these contributions advance the methodological framework for simulating biological membranes at CG resolution, with significant applications to membrane biophysics.","abstract_has_math":false,"creators":["Ramirez Echemendia, Daniel Pastor"],"institution":"Science","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Biological Sciences","degree_department":null,"school":null,"contributors":[],"advisors":["Tieleman, D. Peter"],"committee_chairs":[],"committee_members":["Prenner, Elmar","Kusalik, Peter"],"year":2026,"date_issued":"2026-07-09","date_published":"2026-07-09","updated_at":"2026-07-24T01:30:36Z","subjects":["Biological membranes","Molecular dynamics simulations","Coarse-grained models","Machine learning"],"languages":["en"],"rights":["Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. 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