BMSS

BMSS selects mathematical models to represent dynamic behaviors of gene circuits for construction and optimization of functional genetic circuits.

Key Features:

• Model Library: An extensive library of pre-established mathematical models formulated as ordinary differential equations (ODEs) derived from diverse expression profiles to capture transient dynamics of gene circuits.
• Automated Model Selection: Uses the Akaike Information Criterion (AIC) to select the most suitable model based on characterization data.
• Versatility in Application: Validated across inducible and constitutive expression systems and logic gate configurations (NOT, AND, OR) with agreement to various characterization datasets.
• Reproducibility and Exchangeability: Generates outputs compliant with Synthetic Biology Open Language (SBOL) for gene circuit diagrams and Systems Biology Markup Language (SBML) for model files.
• Modular Programming: Algorithmic components are modular, enabling extension or customization of models and components.
• Programming Language: Implemented in Python.

Scientific Applications:

• Gene circuit design and optimization: Provides model selection to support construction and optimization of functional genetic circuits.
• Characterization of modular biological parts: Enables identification of mathematical representations that describe individual part contributions to circuit performance using characterization data.
• Model standardization and exchange: Produces SBOL and SBML outputs to standardize and share circuit diagrams and model files across studies.

Methodology:

Evaluates characterization data against ODE-formulated candidate models from its library and applies the Akaike Information Criterion (AIC) to select the model that balances goodness-of-fit and complexity.