Stochastic Models and Simulation of Ion Channel Dynamics
The behaviour of ion channels within cardiac and neuronal cells is intrinsically stochastic in nature. When the number of channels is small this stochastic noise is large and can have an impact on the dynamics of the system which is potentially an issue when modelling small neurons and drug block in...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Elsevier
2010
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| _version_ | 1826278815161122816 |
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| author | Dangerfield, C Kay, D Burrage, K ICCS |
| author_facet | Dangerfield, C Kay, D Burrage, K ICCS |
| author_sort | Dangerfield, C |
| collection | OXFORD |
| description | The behaviour of ion channels within cardiac and neuronal cells is intrinsically stochastic in nature. When the number of channels is small this stochastic noise is large and can have an impact on the dynamics of the system which is potentially an issue when modelling small neurons and drug block in cardiac cells. While exact methods correctly capture the stochastic dynamics of a system they are computationally expensive, restricting their inclusion into tissue level models and so approximations to exact methods are often used instead. The other issue in modelling ion channel dynamics is that the transition rates are voltage dependent, adding a level of complexity as the channel dynamics are coupled to the membrane potential. By assuming that such transition rates are constant over each time step, it is possible to derive a stochastic differential equation (SDE), in the same manner as for biochemical reaction networks, that describes the stochastic dynamics of ion channels. While such a model is more computationally efficient than exact methods we show that there are analytical problems with the resulting SDE as well as issues in using current numerical schemes to solve such an equation. We therefore make two contributions: develop a different model to describe the stochastic ion channel dynamics that analytically behaves in the correct manner and also discuss numerical methods that preserve the analytical properties of the model. |
| first_indexed | 2024-03-06T23:49:36Z |
| format | Journal article |
| id | oxford-uuid:722567f1-9a54-4255-87de-65d5645358ab |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:49:36Z |
| publishDate | 2010 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:722567f1-9a54-4255-87de-65d5645358ab2022-03-26T19:48:11ZStochastic Models and Simulation of Ion Channel DynamicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:722567f1-9a54-4255-87de-65d5645358abEnglishSymplectic Elements at OxfordElsevier2010Dangerfield, CKay, DBurrage, KICCSThe behaviour of ion channels within cardiac and neuronal cells is intrinsically stochastic in nature. When the number of channels is small this stochastic noise is large and can have an impact on the dynamics of the system which is potentially an issue when modelling small neurons and drug block in cardiac cells. While exact methods correctly capture the stochastic dynamics of a system they are computationally expensive, restricting their inclusion into tissue level models and so approximations to exact methods are often used instead. The other issue in modelling ion channel dynamics is that the transition rates are voltage dependent, adding a level of complexity as the channel dynamics are coupled to the membrane potential. By assuming that such transition rates are constant over each time step, it is possible to derive a stochastic differential equation (SDE), in the same manner as for biochemical reaction networks, that describes the stochastic dynamics of ion channels. While such a model is more computationally efficient than exact methods we show that there are analytical problems with the resulting SDE as well as issues in using current numerical schemes to solve such an equation. We therefore make two contributions: develop a different model to describe the stochastic ion channel dynamics that analytically behaves in the correct manner and also discuss numerical methods that preserve the analytical properties of the model. |
| spellingShingle | Dangerfield, C Kay, D Burrage, K ICCS Stochastic Models and Simulation of Ion Channel Dynamics |
| title | Stochastic Models and Simulation of Ion Channel Dynamics |
| title_full | Stochastic Models and Simulation of Ion Channel Dynamics |
| title_fullStr | Stochastic Models and Simulation of Ion Channel Dynamics |
| title_full_unstemmed | Stochastic Models and Simulation of Ion Channel Dynamics |
| title_short | Stochastic Models and Simulation of Ion Channel Dynamics |
| title_sort | stochastic models and simulation of ion channel dynamics |
| work_keys_str_mv | AT dangerfieldc stochasticmodelsandsimulationofionchanneldynamics AT kayd stochasticmodelsandsimulationofionchanneldynamics AT burragek stochasticmodelsandsimulationofionchanneldynamics AT iccs stochasticmodelsandsimulationofionchanneldynamics |