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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

Screen shot of the PyRhO GUI (in expanded view) showing the run bar at the top for running simulations and the models tab below where parameters may be adjusted.
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Figure 14: Screen shot of the PyRhO GUI (in expanded view) showing the run bar at the top for running simulations and the models tab below where parameters may be adjusted.

Mentions: A screenshot of the GUI is given in Figure 14 showing the simulation tab for the three-state model. In addition to the parameter fields, the model states diagram is also embedded in the GUI with the equations which describe the opsin's behavior rendered in LaTeX. Similarly, the parameters for each protocol and each simulator are shown on other tabs for easy modification of their values. On the fitting tab, there are also sub-tabs for each of the models, with their respective sets of parameters including tick-boxes to fix parameters and fields for numerical bounds and algebraic constraints to be passed to the fitting algorithms.


PyRhO: A Multiscale Optogenetics Simulation Platform.

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

Screen shot of the PyRhO GUI (in expanded view) showing the run bar at the top for running simulations and the models tab below where parameters may be adjusted.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 14: Screen shot of the PyRhO GUI (in expanded view) showing the run bar at the top for running simulations and the models tab below where parameters may be adjusted.
Mentions: A screenshot of the GUI is given in Figure 14 showing the simulation tab for the three-state model. In addition to the parameter fields, the model states diagram is also embedded in the GUI with the equations which describe the opsin's behavior rendered in LaTeX. Similarly, the parameters for each protocol and each simulator are shown on other tabs for easy modification of their values. On the fitting tab, there are also sub-tabs for each of the models, with their respective sets of parameters including tick-boxes to fix parameters and fields for numerical bounds and algebraic constraints to be passed to the fitting algorithms.

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