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Computational modeling of light activated ion channels

BMC Neuroscience
Springer (Biomed Central Ltd.)
Publication Date
DOI: 10.1186/1471-2202-13-s1-p31
  • Poster Presentation
  • Biology
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  • Engineering
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Computational modeling of light activated ion channels POSTER PRESENTATION Open Access Computational modeling of light activated ion channels Roxana A Stefanescu1*, RG Shivakeshavan2, Paul R Carney1,2, Pramod P Khargonekar3, Sachin S Talathi1,2 From Twenty First Annual Computational Neuroscience Meeting: CNS*2012 Decatur, GA, USA. 21-26 July 2012 Channelrhodpsin-2 (ChR2) is a light sensitive ion chan- nel protein currently investigated for millisecond time scale optogenetic control of neural activity [1]. Two competing mathematical models, a 3-state and a 4-state rate transition model are currently available to mimic the ChR2 photocurrent kinetics [2]. While both models are able to capture the key temporal features of ChR2 photocurrent in response to light stimulation pulses of different intensities and durations, little is known about their efficacy to model the neural response to light sti- mulation protocols of various frequencies and pulse width characteristics. Moreover, it is unclear to what degree the two models can capture the photocurrent kinetics of the recently engineered ChR2 mutants, designed to allow for more precise optical control of neural activity [3]. To address these issues, we investigate a 3-state and a 4-state transition rate model to mimic the photocurrent kinetic of wild type ChR2 (ChR2wt) and a newly vali- dated ChR2 mutant with fast photocurrent kinetics (ChRETA) consistent with the experimental measure- ments in Gunaydin et al. [3]. We incorporate these models into a fast spiking hippocampal interneuron model [4] in order to examine to what degree the neural response to different experimental stimulation protocols can be successfully simulated. We find that the 3-state model can qualitatively repro- duce the neural activity induced by periodic short time interval (2 ms) light pulse stimulation only for low fre- quencies (<20Hz). The model however fails to capture the experimentally observed features of neural response to higher frequencies (80 and

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