Mathematical modelling of oncolytic virotherapy
<p>This thesis is concerned with mathematical modelling of oncolytic virotherapy: the use of genetically modified viruses to selectively spread, replicate and destroy cancerous cells in solid tumours. Traditional spatially-dependent modelling approaches have previously assumed that virus sprea...
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| Formato: | Tesis |
| Lenguaje: | English |
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2013
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| _version_ | 1826297070569390080 |
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| author | Shabala, A |
| author2 | Chapman, S |
| author_facet | Chapman, S Shabala, A |
| author_sort | Shabala, A |
| collection | OXFORD |
| description | <p>This thesis is concerned with mathematical modelling of oncolytic virotherapy: the use of genetically modified viruses to selectively spread, replicate and destroy cancerous cells in solid tumours. Traditional spatially-dependent modelling approaches have previously assumed that virus spread is due to viral diffusion in solid tumours, and also neglect the time delay introduced by the lytic cycle for viral replication within host cells.</p> <p>A deterministic, age-structured reaction-diffusion model is developed for the spatially-dependent interactions of uninfected cells, infected cells and virus particles, with the spread of virus particles facilitated by infected cell motility and delay. Evidence of travelling wave behaviour is shown, and an asymptotic approximation for the wave speed is derived as a function of key parameters.</p> <p>Next, the same physical assumptions as in the continuum model are used to develop an equivalent discrete, probabilistic model for that is valid in the limit of low particle concentrations. This mesoscopic, compartment-based model is then validated against known test cases, and it is shown that the localised nature of infected cell bursts leads to inconsistencies between the discrete and continuum models.</p> <p>The qualitative behaviour of this stochastic model is then analysed for a range of key experimentally-controllable parameters. Two-dimensional simulations of <em>in vivo</em> and <em>in vitro</em> therapies are then analysed to determine the effects of virus burst size, length of lytic cycle, infected cell motility, and initial viral distribution on the wave speed, consistency of results and overall success of therapy.</p> <p>Finally, the experimental difficulty of measuring the effective motility of cells is addressed by considering effective medium approximations of diffusion through heterogeneous tumours. Considering an idealised tumour consisting of periodic obstacles in free space, a two-scale homogenisation technique is used to show the effects of obstacle shape on the effective diffusivity. A novel method for calculating the effective continuum behaviour of random walks on lattices is then developed for the limiting case where microscopic interactions are discrete.</p> |
| first_indexed | 2024-03-07T04:26:00Z |
| format | Thesis |
| id | oxford-uuid:cca2c9bc-cbd4-4651-9b59-8a4dea7245d1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:26:00Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:cca2c9bc-cbd4-4651-9b59-8a4dea7245d12022-03-27T07:23:23ZMathematical modelling of oncolytic virotherapyThesishttp://purl.org/coar/resource_type/c_db06uuid:cca2c9bc-cbd4-4651-9b59-8a4dea7245d1Probability theory and stochastic processesMathematical biologyPartial differential equationsEnglishOxford University Research Archive - Valet2013Shabala, AChapman, SBreward, CWaters, S<p>This thesis is concerned with mathematical modelling of oncolytic virotherapy: the use of genetically modified viruses to selectively spread, replicate and destroy cancerous cells in solid tumours. Traditional spatially-dependent modelling approaches have previously assumed that virus spread is due to viral diffusion in solid tumours, and also neglect the time delay introduced by the lytic cycle for viral replication within host cells.</p> <p>A deterministic, age-structured reaction-diffusion model is developed for the spatially-dependent interactions of uninfected cells, infected cells and virus particles, with the spread of virus particles facilitated by infected cell motility and delay. Evidence of travelling wave behaviour is shown, and an asymptotic approximation for the wave speed is derived as a function of key parameters.</p> <p>Next, the same physical assumptions as in the continuum model are used to develop an equivalent discrete, probabilistic model for that is valid in the limit of low particle concentrations. This mesoscopic, compartment-based model is then validated against known test cases, and it is shown that the localised nature of infected cell bursts leads to inconsistencies between the discrete and continuum models.</p> <p>The qualitative behaviour of this stochastic model is then analysed for a range of key experimentally-controllable parameters. Two-dimensional simulations of <em>in vivo</em> and <em>in vitro</em> therapies are then analysed to determine the effects of virus burst size, length of lytic cycle, infected cell motility, and initial viral distribution on the wave speed, consistency of results and overall success of therapy.</p> <p>Finally, the experimental difficulty of measuring the effective motility of cells is addressed by considering effective medium approximations of diffusion through heterogeneous tumours. Considering an idealised tumour consisting of periodic obstacles in free space, a two-scale homogenisation technique is used to show the effects of obstacle shape on the effective diffusivity. A novel method for calculating the effective continuum behaviour of random walks on lattices is then developed for the limiting case where microscopic interactions are discrete.</p> |
| spellingShingle | Probability theory and stochastic processes Mathematical biology Partial differential equations Shabala, A Mathematical modelling of oncolytic virotherapy |
| title | Mathematical modelling of oncolytic virotherapy |
| title_full | Mathematical modelling of oncolytic virotherapy |
| title_fullStr | Mathematical modelling of oncolytic virotherapy |
| title_full_unstemmed | Mathematical modelling of oncolytic virotherapy |
| title_short | Mathematical modelling of oncolytic virotherapy |
| title_sort | mathematical modelling of oncolytic virotherapy |
| topic | Probability theory and stochastic processes Mathematical biology Partial differential equations |
| work_keys_str_mv | AT shabalaa mathematicalmodellingofoncolyticvirotherapy |