PyRhO 0.9.4

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:

1. to characterize new (and existing) rhodopsins by automatically fitting a minimal set of experimental data to three, four or six-state kinetic models,

2. to simulate these models at the channel, neuron & network levels and

3. 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