{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:case1365173809"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:case1365173809","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"Computational Models of Ex Vivo HIV-1 Dynamics and Fitness Across Scales","abstract":"When modeling multicellular systems, we are often interested in mapping collective macroscopic behavior to cell level characteristics, and vice versa. One of the difficulties in bridging cell level and macroscopic scales is how to interpret the meaning of model parameters when moving from one scale to another. We explore this issue in the context of growth competition assays, used to quantify the ex vivo replicative fitness of two HIV-1 isolates, by developing cell level and macroscopic models, and investigating the relationship between the model parameters at the different scales. We present a detailed, spatially distributed, hybrid stochastic-deterministic cell level model of the dynamics of two competing virus variants, and use it to perform in silico growth competition experiments to better understand how the characteristics of a virus strain influence its ex vivo fitness. To move from the cell level to the macroscopic level, we approximate the cell level model with low resolution and high resolution deterministic macroscopic models, which do not account for cell level biological details or spatial structure. To establish a link between the models at the different scales, we generate data with our cell level model by simulating growth competition experiments between two virus variants with different characteristics, and estimate the parameters of the two macroscopic models from the simulated data sets, approaching the parameter estimation problem from the Bayesian perspective. We investigate how well the macroscopic models specified by the estimated parameters are able to explain the data, and elucidate the intricacies of inferring underlying virus characteristics from macroscopic kinetics by exploring how the parameters of the cell level model relate to those of its macroscopic approximations.","abstract_html":"When modeling multicellular systems, we are often interested in mapping collective macroscopic behavior to cell level characteristics, and vice versa. One of the difficulties in bridging cell level and macroscopic scales is how to interpret the meaning of model parameters when moving from one scale to another. We explore this issue in the context of growth competition assays, used to quantify the ex vivo replicative fitness of two HIV-1 isolates, by developing cell level and macroscopic models, and investigating the relationship between the model parameters at the different scales. We present a detailed, spatially distributed, hybrid stochastic-deterministic cell level model of the dynamics of two competing virus variants, and use it to perform in silico growth competition experiments to better understand how the characteristics of a virus strain influence its ex vivo fitness. To move from the cell level to the macroscopic level, we approximate the cell level model with low resolution and high resolution deterministic macroscopic models, which do not account for cell level biological details or spatial structure. To establish a link between the models at the different scales, we generate data with our cell level model by simulating growth competition experiments between two virus variants with different characteristics, and estimate the parameters of the two macroscopic models from the simulated data sets, approaching the parameter estimation problem from the Bayesian perspective. We investigate how well the macroscopic models specified by the estimated parameters are able to explain the data, and elucidate the intricacies of inferring underlying virus characteristics from macroscopic kinetics by exploring how the parameters of the cell level model relate to those of its macroscopic approximations.","abstract_has_math":false,"creators":["Immonen, Taina Tuulia"],"institution":"Case Western Reserve University School of Graduate Studies","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Applied Mathematics","degree_department":null,"school":null,"contributors":["Calvetti, Daniela","Somersalo, Erkki"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-16","date_published":"2013-08-16","updated_at":"2026-07-24T03:37:16Z","subjects":["Applied Mathematics"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=case1365173809","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Calvetti, Daniela","Somersalo, Erkki"]},{"key":"dc:creator","label":"Author","values":["Immonen, Taina Tuulia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-16"]},{"key":"dc:publisher","label":"Institution","values":["Case Western Reserve University School of Graduate Studies / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Applied Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Case Western Reserve University School of Graduate Studies"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=case1365173809"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["When modeling multicellular systems, we are often interested in mapping collective macroscopic behavior to cell level characteristics, and vice versa. One of the difficulties in bridging cell level and macroscopic scales is how to interpret the meaning of model parameters when moving from one scale to another. We explore this issue in the context of growth competition assays, used to quantify the ex vivo replicative fitness of two HIV-1 isolates, by developing cell level and macroscopic models, and investigating the relationship between the model parameters at the different scales. We present a detailed, spatially distributed, hybrid stochastic-deterministic cell level model of the dynamics of two competing virus variants, and use it to perform in silico growth competition experiments to better understand how the characteristics of a virus strain influence its ex vivo fitness. To move from the cell level to the macroscopic level, we approximate the cell level model with low resolution and high resolution deterministic macroscopic models, which do not account for cell level biological details or spatial structure. To establish a link between the models at the different scales, we generate data with our cell level model by simulating growth competition experiments between two virus variants with different characteristics, and estimate the parameters of the two macroscopic models from the simulated data sets, approaching the parameter estimation problem from the Bayesian perspective. We investigate how well the macroscopic models specified by the estimated parameters are able to explain the data, and elucidate the intricacies of inferring underlying virus characteristics from macroscopic kinetics by exploring how the parameters of the cell level model relate to those of its macroscopic approximations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","20.28 MB"]},{"key":"dc:title","label":"Title","values":["Computational Models of Ex Vivo HIV-1 Dynamics and Fitness Across Scales"]}]}],"canonical_facts":{"dc:contributor":["Calvetti, Daniela","Somersalo, Erkki"],"dc:creator":["Immonen, Taina Tuulia"],"dc:date":["2013-08-16"],"dc:description":["When modeling multicellular systems, we are often interested in mapping collective macroscopic behavior to cell level characteristics, and vice versa. One of the difficulties in bridging cell level and macroscopic scales is how to interpret the meaning of model parameters when moving from one scale to another. We explore this issue in the context of growth competition assays, used to quantify the ex vivo replicative fitness of two HIV-1 isolates, by developing cell level and macroscopic models, and investigating the relationship between the model parameters at the different scales. We present a detailed, spatially distributed, hybrid stochastic-deterministic cell level model of the dynamics of two competing virus variants, and use it to perform in silico growth competition experiments to better understand how the characteristics of a virus strain influence its ex vivo fitness. To move from the cell level to the macroscopic level, we approximate the cell level model with low resolution and high resolution deterministic macroscopic models, which do not account for cell level biological details or spatial structure. To establish a link between the models at the different scales, we generate data with our cell level model by simulating growth competition experiments between two virus variants with different characteristics, and estimate the parameters of the two macroscopic models from the simulated data sets, approaching the parameter estimation problem from the Bayesian perspective. We investigate how well the macroscopic models specified by the estimated parameters are able to explain the data, and elucidate the intricacies of inferring underlying virus characteristics from macroscopic kinetics by exploring how the parameters of the cell level model relate to those of its macroscopic approximations."],"dc:format":["application/pdf","20.28 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=case1365173809"],"dc:language":["English"],"dc:publisher":["Case Western Reserve University School of Graduate Studies / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Applied Mathematics"],"dc:title":["Computational Models of Ex Vivo HIV-1 Dynamics and Fitness Across Scales"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Applied Mathematics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Case Western Reserve University School of Graduate Studies"]},"updated_at":"2026-07-24T03:37:16Z"}