Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models
Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IEEE Digital Library
2016
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| _version_ | 1826286187197759488 |
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| author | Alden, K Timmis, J Andrews, P Veiga-Fernandes, H Coles, M |
| author_facet | Alden, K Timmis, J Andrews, P Veiga-Fernandes, H Coles, M |
| author_sort | Alden, K |
| collection | OXFORD |
| description | Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur. |
| first_indexed | 2024-03-07T01:40:02Z |
| format | Journal article |
| id | oxford-uuid:968b9a3f-5fcb-4afc-a962-08d761f72630 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:40:02Z |
| publishDate | 2016 |
| publisher | IEEE Digital Library |
| record_format | dspace |
| spelling | oxford-uuid:968b9a3f-5fcb-4afc-a962-08d761f726302022-03-26T23:53:36ZExtending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:968b9a3f-5fcb-4afc-a962-08d761f72630EnglishSymplectic Elements at OxfordIEEE Digital Library2016Alden, KTimmis, JAndrews, PVeiga-Fernandes, HColes, MThrough integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur. |
| spellingShingle | Alden, K Timmis, J Andrews, P Veiga-Fernandes, H Coles, M Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title | Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title_full | Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title_fullStr | Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title_full_unstemmed | Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title_short | Extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
| title_sort | extending and applying spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models |
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