Bayesian design of synthetic biological systems

Area of Science:

• Synthetic Biology
• Computational Biology
• Biophysics

Background:

• Synthetic biology design currently separates system inference from construction.
• Developing rational criteria for designing biological systems is challenging.
• Bayesian statistics offers powerful tools for model selection and parameter space exploration.

Purpose of the Study:

• Introduce a novel design framework for synthetic biology.
• Leverage Bayesian model selection for rational system design.
• Integrate design and in silico prototyping stages.

Main Methods:

• Utilize Bayesian model selection for efficient exploration of model and parameter spaces.
• Employ approximate Bayesian computation (ABC) for complex molecular systems.
• Rank models based on their ability to generate desired data.

Main Results:

• Demonstrate a unified approach to inference and design in synthetic biology.
• Showcase the framework's ability to balance model complexity and predictive performance.
• Illustrate applications using adaptive and switch-like systems, and bacterial signaling.

Conclusions:

• The proposed Bayesian framework enhances synthetic biology design by integrating modeling and selection.
• Approximate Bayesian computation facilitates rational design of complex molecular systems.
• Combining design and in silico prototyping leads to improved biological system construction.