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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

The six-state model (re-)fitted to synthetic data generated from parameters derived from six ChR2 photocurrents. It can be seen that the model fits (black lines) almost perfectly fit each synthetic photocurrent using a unified parameter set.
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Figure 11: The six-state model (re-)fitted to synthetic data generated from parameters derived from six ChR2 photocurrents. It can be seen that the model fits (black lines) almost perfectly fit each synthetic photocurrent using a unified parameter set.

Mentions: While there are other differences between some of the original and computed parameters, these may potentially be accounted for by numerical precision issues and the high-dimensional parameter space of the six-state model being under-constrained by the data. For example, a decrease in one parameter may be compensated for by an increase in another such that only latent variables are affected and the fit in the observable current is still good. Inspecting the model fit plots appears to confirm this, as the residual error is very low across the whole set of generated verification photocurrents—at most ±0.5% of the steady-state current and usually considerably less. The entire set of photocurrents fitted to the synthetic data are plotted in Figure 11 and show a very close correspondence to the target (synthetic) photocurrents across the whole set of stimulus intensities.


PyRhO: A Multiscale Optogenetics Simulation Platform.

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

The six-state model (re-)fitted to synthetic data generated from parameters derived from six ChR2 photocurrents. It can be seen that the model fits (black lines) almost perfectly fit each synthetic photocurrent using a unified parameter set.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 11: The six-state model (re-)fitted to synthetic data generated from parameters derived from six ChR2 photocurrents. It can be seen that the model fits (black lines) almost perfectly fit each synthetic photocurrent using a unified parameter set.
Mentions: While there are other differences between some of the original and computed parameters, these may potentially be accounted for by numerical precision issues and the high-dimensional parameter space of the six-state model being under-constrained by the data. For example, a decrease in one parameter may be compensated for by an increase in another such that only latent variables are affected and the fit in the observable current is still good. Inspecting the model fit plots appears to confirm this, as the residual error is very low across the whole set of generated verification photocurrents—at most ±0.5% of the steady-state current and usually considerably less. The entire set of photocurrents fitted to the synthetic data are plotted in Figure 11 and show a very close correspondence to the target (synthetic) photocurrents across the whole set of stimulus intensities.

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