Skip to main content

Fit and characterise rhodopsin photocurrents

Project description

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for PyRhO, version 0.9.4
Filename, size File type Python version Upload date Hashes
Filename, size PyRhO-0.9.4.tar.gz (1.6 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page