Literature DB >> 24560138

Markov modeling of ion channels: implications for understanding disease.

Angelika Lampert1, Alon Korngreen2.   

Abstract

Ion channels are the bridge between the biochemical and electrical domains of our life. These membrane crossing proteins use the electric energy stored in transmembrane ion gradients, which are produced by biochemical activity to generate ionic currents. Each ion channel can be imagined as a small power plant similar to a hydroelectric power station, in which potential energy is converted into electric current. This current drives basically all physiological mechanisms of our body. It is clear that a functional blueprint of these amazing cellular power plants is essential for understanding the principle of all aspects of physiology, particularly neurophysiology. The golden path toward this blueprint starts with the biophysical investigation of ion channel activity and continues through detailed numerical modeling of these channels that will eventually lead to a full system-level description of cellular and organ physiology. Here, we discuss the first two stages of this process focusing on voltage-gated channels, particularly the voltage-gated sodium channel which is neurologically and pathologically important. We first detail the correlations between the known structure of the channel and its activity and describe some pathologies. We then provide a hands-on description of Markov modeling for voltage-gated channels. These two sections of the chapter highlight the dichotomy between the vast amounts of electrophysiological data available on voltage-gated channels and the relatively meager number of physiologically relevant models for these channels.
© 2014 Elsevier Inc. All rights reserved.

Keywords:  Action potential; Ion channel; Kinetic model; Markov chain; Model; Optimisation; Pain; Sodium channel; Voltage clamp; Voltage gated

Mesh:

Substances:

Year:  2014        PMID: 24560138     DOI: 10.1016/B978-0-12-397897-4.00009-7

Source DB:  PubMed          Journal:  Prog Mol Biol Transl Sci        ISSN: 1877-1173            Impact factor:   3.622


  4 in total

Review 1.  Is realistic neuronal modeling realistic?

Authors:  Mara Almog; Alon Korngreen
Journal:  J Neurophysiol       Date:  2016-08-17       Impact factor: 2.714

Review 2.  Bridging scales through multiscale modeling: a case study on protein kinase A.

Authors:  Britton W Boras; Sophia P Hirakis; Lane W Votapka; Robert D Malmstrom; Rommie E Amaro; Andrew D McCulloch
Journal:  Front Physiol       Date:  2015-09-09       Impact factor: 4.566

3.  c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans.

Authors:  Padraig Gleeson; David Lung; Radu Grosu; Ramin Hasani; Stephen D Larson
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-09-10       Impact factor: 6.671

4.  Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1.

Authors:  Sonja Langthaler; Jasmina Lozanović Šajić; Theresa Rienmüller; Seth H Weinberg; Christian Baumgartner
Journal:  Cells       Date:  2022-01-11       Impact factor: 6.600

  4 in total

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