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03885nam a22005535i 4500 |
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978-3-319-30030-6 |
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DE-He213 |
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20210620092658.0 |
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160419s2016 gw | s |||| 0|eng d |
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|a 9783319300306
|9 978-3-319-30030-6
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|a 10.1007/978-3-319-30030-6
|2 doi
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|a QA71-90
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|a PDE
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|a COM014000
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|a PDE
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|a Tveito, Aslak.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models
|h [electronic resource] /
|c by Aslak Tveito, Glenn T. Lines.
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|a 1st ed. 2016.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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|a XVI, 261 p. 129 illus., 30 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Computational Science and Engineering,
|x 1439-7358 ;
|v 111
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|a Preface -- Background: Contents and Method -- One-dimensional calcium release -- Models of open and state blockers -- Two-dimensional calcium release -- Computing theoretical drugs in the two-dimensional case -- Generalized systems -- Calcium-induced calcium release -- Numerical release for CICR -- A prototypical model of an ion channel -- Inactivated ion channels -- A simple model of the sodium channel -- Mutations affecting the mean open time -- The burst mode -- Whole sale action potentials -- .
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|a Open Access
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|a Flow of ions through voltage gated channels can be represented theoretically using stochastic differential equations where the gating mechanism is represented by a Markov model. The flow through a channel can be manipulated using various drugs, and the effect of a given drug can be reflected by changing the Markov model. These lecture notes provide an accessible introduction to the mathematical methods needed to deal with these models. They emphasize the use of numerical methods and provide sufficient details for the reader to implement the models and thereby study the effect of various drugs. Examples in the text include stochastic calcium release from internal storage systems in cells, as well as stochastic models of the transmembrane potential. Well known Markov models are studied and a systematic approach to including the effect of mutations is presented. Lastly, the book shows how to derive the optimal properties of a theoretical model of a drug for a given mutation defined in terms of a Markov model.
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|a Computer mathematics.
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|a Medicine.
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|a Optical data processing.
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|a Computational Science and Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M14026
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|a Biomedicine, general.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/B0000X
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|a Computer Imaging, Vision, Pattern Recognition and Graphics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I22005
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| 700 |
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|a Lines, Glenn T.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783319300290
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| 776 |
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|i Printed edition:
|z 9783319300313
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| 776 |
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|i Printed edition:
|z 9783319807089
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| 830 |
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|a Lecture Notes in Computational Science and Engineering,
|x 1439-7358 ;
|v 111
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|u https://doi.org/10.1007/978-3-319-30030-6
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|a ZDB-2-SXMS
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|a ZDB-2-SOB
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|a Mathematics and Statistics (SpringerNature-11649)
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|a Mathematics and Statistics (R0) (SpringerNature-43713)
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