Four ways to fit an ion channel model
Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example to predict the risks and results of genetic mutations, pharmacological treatments or sur...
| Main Authors: | , , , |
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| Format: | Journal article |
| Sprog: | English |
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Cell Press
2019
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| _version_ | 1826268822216114176 |
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| author | Clerx, M Beattie, K Gavaghan, D Mirams, G |
| author_facet | Clerx, M Beattie, K Gavaghan, D Mirams, G |
| author_sort | Clerx, M |
| collection | OXFORD |
| description | Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example to predict the risks and results of genetic mutations, pharmacological treatments or surgical procedures. These safety-critical applications depend on accurate characterisation of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: (Method 1) fitting model equations directly to time constant, steadystate, and I-V summary curves; (Method 2) fitting by comparing simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves from a range of protocols; and (Method 4) fitting to a single current trace from a short and rapidly-fluctuating voltage clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese Hamster Ovary (CHO) cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that Methods 3 and 4 provide the best predictions on the independent validation set, and that short rapidly-fluctuating protocols like that used in Method 4 can replace much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications. |
| first_indexed | 2024-03-06T21:15:30Z |
| format | Journal article |
| id | oxford-uuid:3fa8a180-3530-4932-b4a1-8c8148756347 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:15:30Z |
| publishDate | 2019 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:3fa8a180-3530-4932-b4a1-8c81487563472022-03-26T14:33:23ZFour ways to fit an ion channel modelJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3fa8a180-3530-4932-b4a1-8c8148756347EnglishSymplectic Elements at OxfordCell Press2019Clerx, MBeattie, KGavaghan, DMirams, GMathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example to predict the risks and results of genetic mutations, pharmacological treatments or surgical procedures. These safety-critical applications depend on accurate characterisation of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: (Method 1) fitting model equations directly to time constant, steadystate, and I-V summary curves; (Method 2) fitting by comparing simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves from a range of protocols; and (Method 4) fitting to a single current trace from a short and rapidly-fluctuating voltage clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese Hamster Ovary (CHO) cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that Methods 3 and 4 provide the best predictions on the independent validation set, and that short rapidly-fluctuating protocols like that used in Method 4 can replace much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications. |
| spellingShingle | Clerx, M Beattie, K Gavaghan, D Mirams, G Four ways to fit an ion channel model |
| title | Four ways to fit an ion channel model |
| title_full | Four ways to fit an ion channel model |
| title_fullStr | Four ways to fit an ion channel model |
| title_full_unstemmed | Four ways to fit an ion channel model |
| title_short | Four ways to fit an ion channel model |
| title_sort | four ways to fit an ion channel model |
| work_keys_str_mv | AT clerxm fourwaystofitanionchannelmodel AT beattiek fourwaystofitanionchannelmodel AT gavaghand fourwaystofitanionchannelmodel AT miramsg fourwaystofitanionchannelmodel |