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        "author": "Benjamin D. Evans",
        "author_email": "ben.d.evans@gmail.com",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Framework :: IPython",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.1",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Life",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Human Machine Interfaces",
            "Topic :: Scientific/Engineering :: Medical Science Apps."
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        "description": "PyRhO - A Virtual Optogenetics Laboratory\n=========================================\n\nA Python module to fit and characterise rhodopsin photocurrents\n\nOptogenetics has become a key tool for understanding the function of neural circuits and controlling their behaviour. 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 rhodopsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. \n\nThe purpose of developing PyRhO is threefold: \n\n(i) to characterize new (and existing) rhodopsins by automatically fitting a minimal set of experimental data to three, four or six-state kinetic models, \n(ii) to simulate these models at the channel, neuron & network levels and \n(iii) provide functional insights through model selection and virtual experiments *in silico*. \n\nThe 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 for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behaviour 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 rhodopsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly improve optogenetics as a tool for transforming biological sciences. \n\nIf you use PyRhO please cite our paper: \n\nEvans, B. D., Jarvis, S., Schultz, S. R. & Nikolic K. (2016) \"PyRhO: A Multiscale Optogenetics Simulation Platform\", *Front. Neuroinform., 10* (8). `doi:10.3389/fninf.2016.00008 <https://dx.doi.org/10.3389/fninf.2016.00008>`_\n\nThe PyRhO project website with additional documentation may be found here: `www.imperial.ac.uk/bio-modelling/pyrho <http://www.imperial.ac.uk/a-z-research/bio-modelling/pyrho>`_",
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        "keywords": "optogenetics rhodopsin opsin brain neuroscience neuron brian jupyter",
        "license": "BSD",
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        "maintainer_email": null,
        "name": "PyRhO",
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        "summary": "Fit and characterise rhodopsin photocurrents",
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