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.
Highlights
Optogenetics is a biotechnology which renders excitable cells light-sensitive by inserting genes which, upon expression, create light-activated ion channels known originally as rhodopsins (Nagel et al, 2003; Boyden et al, 2005)
Computational modeling is core to understanding how light induced ionic transport across cell membranes can be tailored for different applications: from probing cellular physiology to creating new treatments for neurological and psychiatric illnesses. The quest to both expand and refine optogenetics as an effective tool for neuroscience and other areas of physiology requires multiple levels of analysis: from molecular modeling through kinetic models and even network level models. To aid in this effort we propose PyRhO; an integrated suite of opensource, multi-scale computational tools to characterize opsins, rapidly develop and conduct virtual experiments with them in silico
We demonstrate the use of PyRhO in fitting models to Channelrhodopsin-2 (ChR2) data and present results for typical illumination strategies and experimental protocols designed to tease apart the effects of key model parameters
Summary
Optogenetics is a biotechnology which renders excitable cells light-sensitive by inserting genes which, upon expression, create light-activated ion channels known originally as rhodopsins (Nagel et al, 2003; Boyden et al, 2005). Computational modeling is core to understanding how light induced ionic transport across cell membranes can be tailored for different applications: from probing cellular physiology to creating new treatments for neurological and psychiatric illnesses The quest to both expand and refine optogenetics as an effective tool for neuroscience and other areas of physiology requires multiple levels of analysis: from molecular modeling through kinetic models and even network level models. A Graphical User Interface (GUI) for easy navigation through all tools, running of virtual experiments and sharing of results In this way, PyRhO allows the investigator to simulate opsin dynamics on multiple scales from sub-cellular channels, to individual neurons and the dynamics of whole networks. We finish with a discussion of the main benefits of using PyRhO, its limitations to date and planned future developments to extend its capabilities
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