Efficient parameter sensitivity computation for spatially extended reaction networks
Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work,...
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| Format: | Journal article |
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American Institute of Physics
2017
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| _version_ | 1797058700133793792 |
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| author | Lester, C Yates, C Baker, R |
| author_facet | Lester, C Yates, C Baker, R |
| author_sort | Lester, C |
| collection | OXFORD |
| description | Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on stochastic models of spatially-extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios. |
| first_indexed | 2024-03-06T19:54:00Z |
| format | Journal article |
| id | oxford-uuid:24eb525b-8711-49e5-adb3-33303ac49dd5 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:54:00Z |
| publishDate | 2017 |
| publisher | American Institute of Physics |
| record_format | dspace |
| spelling | oxford-uuid:24eb525b-8711-49e5-adb3-33303ac49dd52022-03-26T11:52:55ZEfficient parameter sensitivity computation for spatially extended reaction networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:24eb525b-8711-49e5-adb3-33303ac49dd5Symplectic Elements at OxfordAmerican Institute of Physics2017Lester, CYates, CBaker, RReaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on stochastic models of spatially-extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios. |
| spellingShingle | Lester, C Yates, C Baker, R Efficient parameter sensitivity computation for spatially extended reaction networks |
| title | Efficient parameter sensitivity computation for spatially extended reaction networks |
| title_full | Efficient parameter sensitivity computation for spatially extended reaction networks |
| title_fullStr | Efficient parameter sensitivity computation for spatially extended reaction networks |
| title_full_unstemmed | Efficient parameter sensitivity computation for spatially extended reaction networks |
| title_short | Efficient parameter sensitivity computation for spatially extended reaction networks |
| title_sort | efficient parameter sensitivity computation for spatially extended reaction networks |
| work_keys_str_mv | AT lesterc efficientparametersensitivitycomputationforspatiallyextendedreactionnetworks AT yatesc efficientparametersensitivitycomputationforspatiallyextendedreactionnetworks AT bakerr efficientparametersensitivitycomputationforspatiallyextendedreactionnetworks |