Fit and characterise rhodopsin photocurrents
A Python module to fit and characterise rhodopsin photocurrents
Optogenetics 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.
The purpose of developing PyRhO is threefold:
- to characterize new (and existing) rhodopsins by automatically fitting a minimal set of experimental data to three, four or six-state kinetic models,
- to simulate these models at the channel, neuron & network levels and
- 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 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.
If you use PyRhO please cite our paper:
Evans, 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
The PyRhO project website with additional documentation may be found here: www.imperial.ac.uk/bio-modelling/pyrho
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