GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems

Area of Science:

• Synthetic biology
• Mathematical modeling
• Computational biology

Background:

• Mathematical modeling is crucial for synthetic biology but is often complex and lacks standardization.
• Ad hoc model development hinders reproducibility, critical analysis, and collaboration in synthetic biology research.
• Existing challenges impede the effective design and understanding of synthetic biological systems.

Purpose of the Study:

• To introduce a standardized workflow, GAMES, for developing dynamic models in synthetic biology.
• To streamline the model development process, making it more accessible and reproducible for biologists.
• To facilitate better analysis and refinement of synthetic biological models.

Main Methods:

• The Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow integrates automated and human-in-the-loop processes.
• Systematic consideration of dynamic model development stages: formulation, parameter estimation, identifiability, experimental design, reduction, refinement, and selection.
• Demonstration of the workflow using a case study involving a chemically responsive transcription factor.

Main Results:

• The GAMES workflow provides a structured approach to complex model development.
• The case study successfully illustrates the application of the workflow for analyzing a biological system.
• The workflow addresses challenges in parameter estimation, identifiability, and model selection.

Conclusions:

• The GAMES workflow enhances the process of building and analyzing dynamic models in synthetic biology.
• This generalizable workflow empowers biologists to more easily develop and validate computational models.
• Adoption of GAMES can foster greater collaboration and accelerate advancements in synthetic biology.