Small scale project, 2021 Systems biology simulation

Researcher

Lilija Aprupe-Wehling, PhD student in Modeling of Biological Processes, Centre for Organismal Studies

Initial Problem

• Parameter fitting of a spatial biochemical reaction model using a particle swarm algorithm suffers from two bottlenecks:
• Changing the model, parameters or data used in a fit requires tedious and error prone changes to a Python script
• Running the algorithm on a single cpu core can take too long to converge to a good solution

What we did

• Wrapped the functionality in a user-friendly Python module: sme_contrib.optimize
• Made the fitting thread-safe, then enabled multi-threaded fitting using Python multiprocessing
• Set up a github repository with continuous integration for automated testing and deployment of the code to PyPI
• Provided access to a HPC node for testing the module and evaluating the multi-threaded performance

Results

• Faster and simpler process for the user to set up parameter fitting
• Faster results thanks to the multi-threaded implementation
• This fitting functionality can easily be used and extended by other researchers