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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 four-state and six-state models simulated with ChR2-derived parameters for short-duration pulses. With the four-state photocurrents (A), the peak occurs at the end of the on-phase. This can be seen most clearly in the plot of Pulse duration vs. Time of peak (B) where all points lie on the diagonal. For the six-state photocurrents however, the peaks lag behind the end of the illumination periods slightly (D), due to the transitions to and from the model's extra intermediate states. The inactivation phases (A,D) and magnitude of the peaks (C,F) can be seen to be very similar for both models. For the six-state model, the peak-lag effect can also be observed to diminish as the pulse duration increases (E), demonstrating the practical equivalence of the two models for long stimulation periods.
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Figure 7: The four-state and six-state models simulated with ChR2-derived parameters for short-duration pulses. With the four-state photocurrents (A), the peak occurs at the end of the on-phase. This can be seen most clearly in the plot of Pulse duration vs. Time of peak (B) where all points lie on the diagonal. For the six-state photocurrents however, the peaks lag behind the end of the illumination periods slightly (D), due to the transitions to and from the model's extra intermediate states. The inactivation phases (A,D) and magnitude of the peaks (C,F) can be seen to be very similar for both models. For the six-state model, the peak-lag effect can also be observed to diminish as the pulse duration increases (E), demonstrating the practical equivalence of the two models for long stimulation periods.

Mentions: This procedure returns a Parameters object (from the lmfit module) with the calculated values and plots the resultant model fits over the experimental photocurrents. The entire set of ChR2 data are shown fitted to the six-state model for each flux value spanning two orders of magnitude (ϕ = [2.21 × 1015, 2.65 × 1017] photons · mm−2 · s−1) with the same set of parameters in Figure 5. The lowest and highest intensity photocurrents are also shown in Figure 6 with the model fits for both the three- and four-state models for direct comparison. The six-state model fits are not replotted here as they only exhibit a significant difference to the four-state fits for short pulses, as illustrated in Figure 7.


PyRhO: A Multiscale Optogenetics Simulation Platform.

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

The four-state and six-state models simulated with ChR2-derived parameters for short-duration pulses. With the four-state photocurrents (A), the peak occurs at the end of the on-phase. This can be seen most clearly in the plot of Pulse duration vs. Time of peak (B) where all points lie on the diagonal. For the six-state photocurrents however, the peaks lag behind the end of the illumination periods slightly (D), due to the transitions to and from the model's extra intermediate states. The inactivation phases (A,D) and magnitude of the peaks (C,F) can be seen to be very similar for both models. For the six-state model, the peak-lag effect can also be observed to diminish as the pulse duration increases (E), demonstrating the practical equivalence of the two models for long stimulation periods.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: The four-state and six-state models simulated with ChR2-derived parameters for short-duration pulses. With the four-state photocurrents (A), the peak occurs at the end of the on-phase. This can be seen most clearly in the plot of Pulse duration vs. Time of peak (B) where all points lie on the diagonal. For the six-state photocurrents however, the peaks lag behind the end of the illumination periods slightly (D), due to the transitions to and from the model's extra intermediate states. The inactivation phases (A,D) and magnitude of the peaks (C,F) can be seen to be very similar for both models. For the six-state model, the peak-lag effect can also be observed to diminish as the pulse duration increases (E), demonstrating the practical equivalence of the two models for long stimulation periods.
Mentions: This procedure returns a Parameters object (from the lmfit module) with the calculated values and plots the resultant model fits over the experimental photocurrents. The entire set of ChR2 data are shown fitted to the six-state model for each flux value spanning two orders of magnitude (ϕ = [2.21 × 1015, 2.65 × 1017] photons · mm−2 · s−1) with the same set of parameters in Figure 5. The lowest and highest intensity photocurrents are also shown in Figure 6 with the model fits for both the three- and four-state models for direct comparison. The six-state model fits are not replotted here as they only exhibit a significant difference to the four-state fits for short pulses, as illustrated in Figure 7.

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