Double Two-State Opsin Model With Autonomous Parameter Inference

被引:4
|
作者
Schoeters, Ruben [1 ]
Tarnaud, Thomas [1 ]
Martens, Luc [1 ]
Joseph, Wout [1 ]
Raedt, Robrecht [2 ]
Tanghe, Emmeric [1 ]
机构
[1] Ghent Univ IMEC, Dept Informat Technol INTEL, WAVES, Ghent, Belgium
[2] Univ Ghent, Inst Neurosci, Dept Neurol, 4BRAIN, Ghent, Belgium
关键词
computational optogenetics; computational efficiency; channelrhodopsin-H134R; MerMAID; model fitting; LIGHT-DARK ADAPTATION; CHANNELRHODOPSIN; OPTOGENETICS; EVOLUTION;
D O I
10.3389/fncom.2021.688331
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Effective Potential of a Two-State Model for Molecular Motor
    HAN Ying-Rong~1 ZHAO Tong-Jun~(1
    Communications in Theoretical Physics, 2005, 43 (02) : 377 - 381
  • [42] Two-state model of energy dissipation at metal surfaces
    Tully, John C.
    JOURNAL OF CHEMICAL PHYSICS, 2024, 160 (12):
  • [43] Two-state model for sub-exponential fluorescence
    Huber, DL
    JOURNAL OF LUMINESCENCE, 2000, 86 (02) : 95 - 99
  • [44] Structure of liquid water: Is the two-state model operative?
    Glinski, Jacek
    Burakowski, Andrzej
    CHEMICAL PHYSICS LETTERS, 2011, 508 (4-6) : 210 - 214
  • [45] A two-state model for the kinetics of competitive radioligand binding
    Guo, Dong
    Peletier, Lambertus A.
    Bridge, Lloyd
    Keur, Wesley
    de Vries, Henk
    Zweemer, Annelien
    Heitman, Laura H.
    IJzerman, Adriaan P.
    BRITISH JOURNAL OF PHARMACOLOGY, 2018, 175 (10) : 1719 - 1730
  • [46] The rejuvenation effect in the two-state random energy model
    Kawasaki, M
    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 2001, 70 (06) : 1762 - 1767
  • [47] PERFORMANCE ANALYSIS OF A TWO-STATE QUEUEING MODEL WITH RETRIALS
    Singla, Neelam
    Kalra, Sonia
    JOURNAL OF RAJASTHAN ACADEMY OF PHYSICAL SCIENCES, 2018, 17 (1-2): : 81 - 99
  • [48] Two-state teleportation
    Henderson, L.
    Hardy, L.
    Vedral, V.
    Physical Review A - Atomic, Molecular, and Optical Physics, 2000, 61 (06): : 062306 - 062301
  • [49] Growth and Shortening of Microtubules A TWO-STATE MODEL APPROACH
    Zhang, Yunxin
    JOURNAL OF BIOLOGICAL CHEMISTRY, 2011, 286 (45) : 39439 - 39449
  • [50] A two-state neuronal model with alternating exponential excitation
    Ratanov, Nikita
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (05) : 3411 - 3434