Channelpedia is a knowledge-based framework centered on genetically expressed ion channel models and it encourages researchers of the field to contribute, build and refine the information through an interactive wiki-like interface. It is web-based, freely accessible and currently contains 180 annotated ion channels, electrophysiology data for all Kv ion channels and 50 Hodgkin-Huxley models.

Click on graphs for experimental data

Channelpedia provides an ideal platform to collectively build ion channel knowledge base by accommodating both structured and unstructured data. The current version of Channelpedia contains the following sections : Introduction, Experimental data, Genes, Ontologies, Interactions, Structure, Expression, Distribution, Function, Kinetics and Models.

Newly published literature related to ion channels is automatically queried every week from PubMed and added to respective categories. Currently, Channelpedia contains about 180,000 abstracts related to ion channels from Pubmed.

In progress

Kinetic characterization of Ih, K2P, Kir and Na channels at 25°C and 35°C is in progress.




Data sources

Contributors and existing online resources are the two main sources of data (Terms of Use and Data License). The unstructured data is populated by contributors, who can freely edit formatted text and upload images without violating copyright agreements. Structured data contains data from existing online resources managed by administrator using automated scripts. Experimental data and analyzed data from literature is uploaded by contributors and stored as structured data.

Framework Datasource

HH models

Building models for each ion channel is one of the main goal of Channelpedia. Data from numerous voltage clamp experiments are already available in current literature. Constructing a HH model from this experimental data requires 1) Parameter identification 2) Digitization 3) H-H model fitting and 4) Parameter readjustment. Details...


Abdeladim Elhamdani, Adnan Abid, Albert Gidon, Daniel Keller, Emmanuelle Logette, Enrico Scantamburlo, Etay Hay, Geetanjali Saha, Georges Khazen, James G. King, Luca Gambazzi, Martin Telefont, Maurizio Pezzoli, Meghana Katiki,Sean Hill, Shaul Druckmann, Shruti Muralidhar, Srikanth Ramaswamy, Thomas Mccolgan, Michael Schartner