BMSS2.0 (New!)

A unified python library for model building, selection, and analysis

Supported Features:

  • Database-driven to allow one to interactively retrieve and store models from/into SQL databases to ensure reproducibility
  • Standard model creation and simulation
  • Parameter estimation using Bayesian Inference based on Markov chain Monte Carlo and other global optimizers
  • Trace analysis for a posteriori identifiability
  • A priori structural identifiability to highlight states to be measured for making parameters identifiable
  • Automated model selection that best balances goodness-of-fit and complexity
  • Global sensitivity analysis to determine sensitive parameters for tuning
  • Support parsing of models using standard file formats such as SBML, SEDML, and COMBINE archive

The python package is available for download on GitHub


Examples and Tutorials are available for references


A webpage for users to browse and download the models in SBML format

Contact

Feel free to contact us at [email protected] if you have any questions or to report any bugs.

BMSS (BioModel Selection System)

BMSS, an automated BioModel Selection System for gene circuit designs, supports bio-model automated fitting and selection processes, providing a means to efficiently derive the best model candidate that could capture the transient (time-series) dynamic profiles of a bio-part or device using characterization data. This system is an open source platform which is implemented in Python. The developed Python Package BMSSlib is available for downloading from GitHub. The package supports three routinely used gene regulatory systems: inducible system, constitutive system (single dataset or multiple datasets) and logic gate system (NOT, AND, and OR gates).


A comprehensive user manual


Check our video tutorial on how to use the BMSS software


Here you can find the source code of the BMSS software


The Readme file in GitHub


The installation instructions in GitHub


The execution instructions in GitHub


How to cite

Refer to the published paper for citation information.