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PyRhO: A Multiscale Optogenetics Simulation Platform.

Evans BD, Jarvis S, Schultz SR, Nikolic K - Front Neuroinform (2016)

Bottom Line: The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage.The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system.This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop.

View Article: PubMed Central - PubMed

Affiliation: Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Imperial College London London, UK.

ABSTRACT
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.

No MeSH data available.


Related in: MedlinePlus

An example six-state model fit to ChR2 data (at ϕ = 2.21 × 1015 photons · mm−2 · s−1) with the accompanying residual error expressed as a percentage of the steady-state current.
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Figure 9: An example six-state model fit to ChR2 data (at ϕ = 2.21 × 1015 photons · mm−2 · s−1) with the accompanying residual error expressed as a percentage of the steady-state current.

Mentions: Having fit a model, it may be easily characterized by plotting how the light-dependent transition rates vary as a function of flux (based on the Hill equation) along with light-independent transition rates as shown in Figure 8. An individual fit is shown in more detail with the residual error for ϕ = 2.21 × 1015 photons · mm−2 · s−1 in Figure 9. To provide insight into the model's kinetics, PyRhO also offers state variable plots. The evolution of the six-state model corresponding to the fit in Figure 9 is given in Figure 10.


PyRhO: A Multiscale Optogenetics Simulation Platform.

Evans BD, Jarvis S, Schultz SR, Nikolic K - Front Neuroinform (2016)

An example six-state model fit to ChR2 data (at ϕ = 2.21 × 1015 photons · mm−2 · s−1) with the accompanying residual error expressed as a percentage of the steady-state current.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834562&req=5

Figure 9: An example six-state model fit to ChR2 data (at ϕ = 2.21 × 1015 photons · mm−2 · s−1) with the accompanying residual error expressed as a percentage of the steady-state current.
Mentions: Having fit a model, it may be easily characterized by plotting how the light-dependent transition rates vary as a function of flux (based on the Hill equation) along with light-independent transition rates as shown in Figure 8. An individual fit is shown in more detail with the residual error for ϕ = 2.21 × 1015 photons · mm−2 · s−1 in Figure 9. To provide insight into the model's kinetics, PyRhO also offers state variable plots. The evolution of the six-state model corresponding to the fit in Figure 9 is given in Figure 10.

Bottom Line: The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage.The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system.This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop.

View Article: PubMed Central - PubMed

Affiliation: Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Imperial College London London, UK.

ABSTRACT
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.

No MeSH data available.


Related in: MedlinePlus