BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis

Abstract

Modelling in Synthetic Biology constitutes a powerful tool in our continuous search for improved performance with rational Design-Build-Test-Learn approach. In particular, kinetic models unravel system dynamics, enabling system analysis for guiding experimental designs. However, a systematic yet modular pipeline that allows one to identify the "right" model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we introduce a unified python package, BMSS2, which offers the principal tools in model development and analysis - simulation, Bayesian parameter inference, global sensitivity analysis, with an emphasis on model selection, and a priori and a posteriori identifiability analysis. The whole package is database-driven to support interactive retrieving and storing of models for reusability. This allows ease of manipulation and deposition of models for the model selection and analysis process, thus enabling better utilization of models in guiding experimental designs.

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

“…The code can be assessed on GitHub, enabling reproducibility and reusability of the workflow. We anticipate that GAMES could be adapted to previously developed software packages such as Data2Dynamics 55 , PottersWheel 56 , COPASI 57 (a Complex Pathway Simulator), and BMSS 58, 59 (an automated Biomodel Selection System) to accomplish specific computational tasks, and that this adaptation could improve computational efficiency compared to the code in this tutorial. Our code is intended primarily to provide an example of how to implement GAMES.…”