GSoC Project Idea 9: Running FindSim experiments on cloud servers


Various XML based formats such as SBML, NeuroML etc are available which makes developing a neuronal signaling model user as well as machine friendly. The most effective way of validating a model is to compare the readouts of a simulation with that of an actual experiment to see how closely the simulation output fits the real experiment readout. Framework for Integrating Neuronal Data and Signaling Models (FindSim) is a tool that enable systematic validation and optimization of a neuronal signaling model by anchoring a model to actual experiment dataset.

We are developing a web-based tool which allow users to use the FindSim pipeline in a user-friendly way. Running a simulation experiment can be a computing extensive processes depending on the size of the model and various parameters involved. The computation time can range from a few seconds to a few hours. Currently, the web server is able to run small model with acceptable efficiency and run time. However, for large models it takes a lot of time. Our plan is to employ a high performance cloud server to run the simulation job (may be use Amazon web services or nsg or anything else) and use the web server to store the data and serve the web tool, for optimal distribution of the workload and smooth functioning of the web tool. The aim of the GSoC project will be to set up data exchange between the web server and the cloud computing server.

Things to be done:

  • Setting up a computation server using docker/?, and installing moose and FindSim on it
  • Implementing a RESTful API to set up talk between the web server and the computation server
  • Sending experiment run request to the computation server and receiving the output on the web server
  • Implementing JavaScript for visualizing the results of the simulation in real time.

Required Skill Set

  • Python, Network/Web programming
  • Familiarity with network streaming, serialization (xml, json), RESTful API.
  • PHP and C++ is plus.

Mentors: Surbhit Wagle ( ), Upinder Bhalla ( )

For more details about the FindSim please refer to following links: