Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models

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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.


Offers an accessible introduction to an important theory in a field of growing relevance Includes complete details of models and methods, enabling the reader to implement and study the models under consideration Pursues a systematic approach to studying stochastic release mechanisms where the gating is governed by a Markov model Includes supplementary material: sn.pub/extras

Inhalt
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.-

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783319300290
    • Genre Information Technology
    • Auflage 1st ed.
    • Lesemotiv Verstehen
    • Anzahl Seiten 261
    • Größe H241mm x B169mm x T20mm
    • Jahr 2016
    • EAN 9783319300290
    • Format Fester Einband
    • ISBN 978-3-319-30029-0
    • Titel Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models
    • Autor Aslak Tveito , Glenn T. Lines
    • Untertitel Lecture Notes in Computational Science and Engineering 111
    • Gewicht 543g
    • Herausgeber Springer International Publishing
    • Sprache Englisch

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