Improving our mechanistic understanding of cell cycle dynamics
<p>The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. They facilitate a detailed understanding of cell cycle c...
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| _version_ | 1826308685396180992 |
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| author | Lang, P |
| author2 | Novak, B |
| author_facet | Novak, B Lang, P |
| author_sort | Lang, P |
| collection | OXFORD |
| description | <p>The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. They facilitate a detailed understanding of cell cycle control mechanisms and are in part also able to aggregate this knowledge into full cell cycle models that explain periodic cell cycle oscillations. This dissertation aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability.</p>
<p>Presented is a core model structure of the full cell cycle that qualitatively explains the behaviour of unperturbed and perturbed cells. Using rule-based model descriptions, the core model was conveniently extended by a DNA damage checkpoint and a separation in a nuclear and cytoplasmic compartment. The dissertation conceptualises a methodology for generating highly informative validation data, including cell cycle perturbation with CRISPR interference. More specifically, time courses of 292 features as measured by indirect immunofluorescence imaging experiments are reconstructed from single cell snapshot data using the reCAT algorithm. This data and the cell cycle model are then cast into the PEtab format for specifying parameter estimation problems in biochemical reaction networks. By combining a powerful hybrid global-local optimiser with hierarchical optimisation of parameters, a cell cycle model that explains the validation data is presented. The PEtab specification allows any modeler to reuse the model, the data and/or the optimisation results.</p>
<p>Further experimental conditions, for instance in form of CRISPR interference, are expected to significantly improve parameter identifiability and provide a way for testing the predictive power of the model. Given the central role of the cell cycle in health and disease, such a predictive model may aid in the discovery of new therapeutic targets.</p> |
| first_indexed | 2024-03-07T07:23:02Z |
| format | Thesis |
| id | oxford-uuid:888439ad-99ac-4e89-9473-cc4864cf1e94 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:23:02Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oxford-uuid:888439ad-99ac-4e89-9473-cc4864cf1e942022-10-28T13:23:43ZImproving our mechanistic understanding of cell cycle dynamicsThesishttp://purl.org/coar/resource_type/c_db06uuid:888439ad-99ac-4e89-9473-cc4864cf1e94cell cyclesystems biologyEnglishHyrax Deposit2022Lang, PNovak, BPenas, DBanga, JWeindl, D<p>The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. They facilitate a detailed understanding of cell cycle control mechanisms and are in part also able to aggregate this knowledge into full cell cycle models that explain periodic cell cycle oscillations. This dissertation aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability.</p> <p>Presented is a core model structure of the full cell cycle that qualitatively explains the behaviour of unperturbed and perturbed cells. Using rule-based model descriptions, the core model was conveniently extended by a DNA damage checkpoint and a separation in a nuclear and cytoplasmic compartment. The dissertation conceptualises a methodology for generating highly informative validation data, including cell cycle perturbation with CRISPR interference. More specifically, time courses of 292 features as measured by indirect immunofluorescence imaging experiments are reconstructed from single cell snapshot data using the reCAT algorithm. This data and the cell cycle model are then cast into the PEtab format for specifying parameter estimation problems in biochemical reaction networks. By combining a powerful hybrid global-local optimiser with hierarchical optimisation of parameters, a cell cycle model that explains the validation data is presented. The PEtab specification allows any modeler to reuse the model, the data and/or the optimisation results.</p> <p>Further experimental conditions, for instance in form of CRISPR interference, are expected to significantly improve parameter identifiability and provide a way for testing the predictive power of the model. Given the central role of the cell cycle in health and disease, such a predictive model may aid in the discovery of new therapeutic targets.</p> |
| spellingShingle | cell cycle systems biology Lang, P Improving our mechanistic understanding of cell cycle dynamics |
| title | Improving our mechanistic understanding of cell cycle dynamics |
| title_full | Improving our mechanistic understanding of cell cycle dynamics |
| title_fullStr | Improving our mechanistic understanding of cell cycle dynamics |
| title_full_unstemmed | Improving our mechanistic understanding of cell cycle dynamics |
| title_short | Improving our mechanistic understanding of cell cycle dynamics |
| title_sort | improving our mechanistic understanding of cell cycle dynamics |
| topic | cell cycle systems biology |
| work_keys_str_mv | AT langp improvingourmechanisticunderstandingofcellcycledynamics |