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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

Schematic of the PyRhO work-flow. Model parameters may be user supplied, initialized with defaults, or optionally derived from data. The user then selects the number of states in the model, the stimulation protocol and the simulation engine to start running virtual experiments.
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Figure 1: Schematic of the PyRhO work-flow. Model parameters may be user supplied, initialized with defaults, or optionally derived from data. The user then selects the number of states in the model, the stimulation protocol and the simulation engine to start running virtual experiments.

Mentions: The simulation architecture is designed around three layers of abstraction: models, protocols and simulators. These layers are illustrated in the work-flow schematic of Figure 1 along with the other major components of PyRhO. Each layer contains families of classes to create a uniform interface for each subclass, for example, the differences in setting the light-dependent transition rates of the three models are shielded from the user by endowing each opsin model subclass with the method setLight(). A similar approach is taken with the other layers providing a common set of member variables and methods, making usage consistent and providing a framework for future development of new subclasses (i.e., additional kinetic models, stimulation protocols, and simulation platforms).


PyRhO: A Multiscale Optogenetics Simulation Platform.

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

Schematic of the PyRhO work-flow. Model parameters may be user supplied, initialized with defaults, or optionally derived from data. The user then selects the number of states in the model, the stimulation protocol and the simulation engine to start running virtual experiments.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Schematic of the PyRhO work-flow. Model parameters may be user supplied, initialized with defaults, or optionally derived from data. The user then selects the number of states in the model, the stimulation protocol and the simulation engine to start running virtual experiments.
Mentions: The simulation architecture is designed around three layers of abstraction: models, protocols and simulators. These layers are illustrated in the work-flow schematic of Figure 1 along with the other major components of PyRhO. Each layer contains families of classes to create a uniform interface for each subclass, for example, the differences in setting the light-dependent transition rates of the three models are shielded from the user by endowing each opsin model subclass with the method setLight(). A similar approach is taken with the other layers providing a common set of member variables and methods, making usage consistent and providing a framework for future development of new subclasses (i.e., additional kinetic models, stimulation protocols, and simulation platforms).

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