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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 three-, four-, and six-state functional Markov models of opsins.
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Figure 2: The three-, four-, and six-state functional Markov models of opsins.

Mentions: At the core of PyRhO are three functional Markov models of opsin kinetics, namely the three-, four- (Nikolic et al., 2009), and six-state (Grossman et al., 2013) models. We note that very similar models have been investigated in several other studies (Gradmann et al., 2002; Nagel et al., 2003; Hegemann et al., 2005; Ishizuka et al., 2006; Bamann et al., 2008; Ernst et al., 2008; Foutz et al., 2012; Williams et al., 2013) but used our earlier models as a starting point as we have since extended them and unified their notation. These models vary in complexity providing a range in the trade-off between biological accuracy and computational efficiency to choose from. The key features of these models, including an outline of their strengths and weaknesses, are summarized in Table 1 with accompanying illustrations in Figure 2. Since their original formulation, the models have been extended to encompass additional parameter dependencies, better fit the experimental data and use a consistent notation, with the full model descriptions given in Table 2. An analytic solution for the three-state model was also calculated and is included in the Appendix.


PyRhO: A Multiscale Optogenetics Simulation Platform.

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

The three-, four-, and six-state functional Markov models of opsins.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: The three-, four-, and six-state functional Markov models of opsins.
Mentions: At the core of PyRhO are three functional Markov models of opsin kinetics, namely the three-, four- (Nikolic et al., 2009), and six-state (Grossman et al., 2013) models. We note that very similar models have been investigated in several other studies (Gradmann et al., 2002; Nagel et al., 2003; Hegemann et al., 2005; Ishizuka et al., 2006; Bamann et al., 2008; Ernst et al., 2008; Foutz et al., 2012; Williams et al., 2013) but used our earlier models as a starting point as we have since extended them and unified their notation. These models vary in complexity providing a range in the trade-off between biological accuracy and computational efficiency to choose from. The key features of these models, including an outline of their strengths and weaknesses, are summarized in Table 1 with accompanying illustrations in Figure 2. Since their original formulation, the models have been extended to encompass additional parameter dependencies, better fit the experimental data and use a consistent notation, with the full model descriptions given in Table 2. An analytic solution for the three-state model was also calculated and is included in the Appendix.

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