{"id":{"repo_id":"abertay","oai_identifier":"oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047"},"canonical_url":"https://search.dev.ndltd.org/etd/abertay/oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047","repository":{"repo_id":"abertay","name":"Abertay University","base_url":"https://rke.abertay.ac.uk/ws/oai"},"display":{"title":"Data-driven modelling and optimised reverse engineering of complex dynamical systems in cancer research","abstract":"Biological systems typically generate complex data that encapsulate the dynamics of interactions among measurables over time. To support the formation of insights into time series data from a biological system, there is a requirement to develop new methods that can analyse and translate such complex data into a form that allows trends, patterns, and predictions to be easily viewed, verified and tested. Here, a suite of novel analytical and matrix-based techniques for dynamical systems modelling are developed that are time-efficient and data-driven. These techniques facilitate a range of scientific analyses through novel matrix-based system identification and parameter estimation methods. The inference techniques are fast, optimised, and do not require a priori information to successfully infer network of interactions or automatically construct data-consistent models from data. Two distinct principal (Jacobian and power-law) models (solutions) that are data-consistent may be constructed from a single time series data set. A recast technique has also been developed to reconstruct either one of the principal models from the other, providing support for model interoperability and multiple model integration. <br/><br/>The thesis demonstrates the effectiveness of a new theoretical framework developed to incorporate a modelling and visualization pipeline able to deal with a wide range of time-series data sets relating to complex biological systems. The integrated framework is able to infer and depict interaction networks implicit in time series data in just a matter of seconds and then display the evolution of that network dynamics in response to network perturbation such as drug treatments. Beyond this, there is a broader contribution to the field of biochemical system theory (BST), evidenced by establishing methods for transforming a constructed jacobian model to equivalent power-law models, and vice versa. The effectiveness of these new techniques is demonstrated using artificial time series data samples, simulated pseudo-data of biologically plausible models of real biological systems, and real experimental data derived from biological experiments.","abstract_html":"Biological systems typically generate complex data that encapsulate the dynamics of interactions among measurables over time. To support the formation of insights into time series data from a biological system, there is a requirement to develop new methods that can analyse and translate such complex data into a form that allows trends, patterns, and predictions to be easily viewed, verified and tested. Here, a suite of novel analytical and matrix-based techniques for dynamical systems modelling are developed that are time-efficient and data-driven. These techniques facilitate a range of scientific analyses through novel matrix-based system identification and parameter estimation methods. The inference techniques are fast, optimised, and do not require a priori information to successfully infer network of interactions or automatically construct data-consistent models from data. Two distinct principal (Jacobian and power-law) models (solutions) that are data-consistent may be constructed from a single time series data set. A recast technique has also been developed to reconstruct either one of the principal models from the other, providing support for model interoperability and multiple model integration. &lt;br/&gt;&lt;br/&gt;The thesis demonstrates the effectiveness of a new theoretical framework developed to incorporate a modelling and visualization pipeline able to deal with a wide range of time-series data sets relating to complex biological systems. The integrated framework is able to infer and depict interaction networks implicit in time series data in just a matter of seconds and then display the evolution of that network dynamics in response to network perturbation such as drug treatments. Beyond this, there is a broader contribution to the field of biochemical system theory (BST), evidenced by establishing methods for transforming a constructed jacobian model to equivalent power-law models, and vice versa. The effectiveness of these new techniques is demonstrated using artificial time series data samples, simulated pseudo-data of biologically plausible models of real biological systems, and real experimental data derived from biological experiments.","abstract_has_math":false,"creators":["Idowu, Michael A."],"institution":"University of Abertay Dundee","degree_name":"PhD","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Bown, James"],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-7","date_published":"2013-7","updated_at":"2026-07-24T00:50:10Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047"],"render_values":[{"text":"oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047","href":null,"code":true}]}]},"links":{"outbound_url":"https://rke.abertay.ac.uk/en/studentTheses/c025b467-b317-4dbf-9c52-96a6e9d75047","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bown, James"]},{"key":"dc:creator","label":"Author","values":["Idowu, Michael A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-7"]},{"key":"dc:date.issued","label":"Date","values":["2013-7"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["SDI"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Abertay Dundee"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://rke.abertay.ac.uk/en/studentTheses/c025b467-b317-4dbf-9c52-96a6e9d75047"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047","https://rke.abertay.ac.uk/en/studentTheses/c025b467-b317-4dbf-9c52-96a6e9d75047"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://rke.abertay.ac.uk/files/15224349/Idowu_2013_Data_driven_modelling_and_optimised_PhD.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Biological systems typically generate complex data that encapsulate the dynamics of interactions among measurables over time. To support the formation of insights into time series data from a biological system, there is a requirement to develop new methods that can analyse and translate such complex data into a form that allows trends, patterns, and predictions to be easily viewed, verified and tested. Here, a suite of novel analytical and matrix-based techniques for dynamical systems modelling are developed that are time-efficient and data-driven. These techniques facilitate a range of scientific analyses through novel matrix-based system identification and parameter estimation methods. The inference techniques are fast, optimised, and do not require a priori information to successfully infer network of interactions or automatically construct data-consistent models from data. Two distinct principal (Jacobian and power-law) models (solutions) that are data-consistent may be constructed from a single time series data set. A recast technique has also been developed to reconstruct either one of the principal models from the other, providing support for model interoperability and multiple model integration. <br/><br/>The thesis demonstrates the effectiveness of a new theoretical framework developed to incorporate a modelling and visualization pipeline able to deal with a wide range of time-series data sets relating to complex biological systems. The integrated framework is able to infer and depict interaction networks implicit in time series data in just a matter of seconds and then display the evolution of that network dynamics in response to network perturbation such as drug treatments. Beyond this, there is a broader contribution to the field of biochemical system theory (BST), evidenced by establishing methods for transforming a constructed jacobian model to equivalent power-law models, and vice versa. The effectiveness of these new techniques is demonstrated using artificial time series data samples, simulated pseudo-data of biologically plausible models of real biological systems, and real experimental data derived from biological experiments."]},{"key":"dc:title","label":"Title","values":["Data-driven modelling and optimised reverse engineering of complex dynamical systems in cancer research"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bown, James"],"dc:creator":["Idowu, Michael A."],"dc:date":["2013-7"],"dc:date.issued":["2013-7"],"dc:description.abstract":["Biological systems typically generate complex data that encapsulate the dynamics of interactions among measurables over time. To support the formation of insights into time series data from a biological system, there is a requirement to develop new methods that can analyse and translate such complex data into a form that allows trends, patterns, and predictions to be easily viewed, verified and tested. Here, a suite of novel analytical and matrix-based techniques for dynamical systems modelling are developed that are time-efficient and data-driven. These techniques facilitate a range of scientific analyses through novel matrix-based system identification and parameter estimation methods. The inference techniques are fast, optimised, and do not require a priori information to successfully infer network of interactions or automatically construct data-consistent models from data. Two distinct principal (Jacobian and power-law) models (solutions) that are data-consistent may be constructed from a single time series data set. A recast technique has also been developed to reconstruct either one of the principal models from the other, providing support for model interoperability and multiple model integration. <br/><br/>The thesis demonstrates the effectiveness of a new theoretical framework developed to incorporate a modelling and visualization pipeline able to deal with a wide range of time-series data sets relating to complex biological systems. The integrated framework is able to infer and depict interaction networks implicit in time series data in just a matter of seconds and then display the evolution of that network dynamics in response to network perturbation such as drug treatments. Beyond this, there is a broader contribution to the field of biochemical system theory (BST), evidenced by establishing methods for transforming a constructed jacobian model to equivalent power-law models, and vice versa. The effectiveness of these new techniques is demonstrated using artificial time series data samples, simulated pseudo-data of biologically plausible models of real biological systems, and real experimental data derived from biological experiments."],"dc:identifier":["oai:rke.abertay.ac.uk:studenttheses/c025b467-b317-4dbf-9c52-96a6e9d75047","https://rke.abertay.ac.uk/en/studentTheses/c025b467-b317-4dbf-9c52-96a6e9d75047"],"dc:identifier.uri":["https://rke.abertay.ac.uk/files/15224349/Idowu_2013_Data_driven_modelling_and_optimised_PhD.pdf"],"dc:language":["eng"],"dc:publisher.department":["SDI"],"dc:publisher.institution":["University of Abertay Dundee"],"dc:relation.isreferencedby":["https://rke.abertay.ac.uk/en/studentTheses/c025b467-b317-4dbf-9c52-96a6e9d75047"],"dc:title":["Data-driven modelling and optimised reverse engineering of complex dynamical systems in cancer research"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral Thesis"],"dc:type.qualificationname":["PhD"]},"updated_at":"2026-07-24T00:50:10Z"}