Context-Aware Technology Mapping in Genetic Design Automation.

Area of Science:

• Synthetic Biology
• Computational Biology
• Genetic Engineering

Background:

• Genetic design automation (GDA) tools aim to accelerate synthetic biology circuit design.
• Limited predictive power of current GDA tools leads to discrepancies between in silico and in vivo genetic circuit performance.
• Cellular context effects, including crosstalk and regulator titration, are major contributors to these performance deviations.

Purpose of the Study:

• To incorporate cellular context effects into computational models for GDA tools.
• To enhance the predictive power of GDA tools by accounting for intracellular environment and component crosstalk.
• To improve the deployment and reliability of synthetic biology circuits designed using GDA.

Main Methods:

• Utilized fine-grained thermodynamic models of promoter activity.
• Accounted for crosstalk due to limited regulator specificity.
• Modeled titration of circuit regulators to off-target host genome binding sites.
• Employed branch-and-bound techniques to manage computational complexity during technology mapping.

Main Results:

• Demonstrated a method to incorporate crosstalk and regulator titration into GDA models.
• Showcased compensation for increased computational complexity using branch-and-bound techniques.
• Analyzed the impact of crosstalk intensity and distribution on circuit performance using Cello's device library as a case study.

Conclusions:

• Accounting for cellular context effects significantly improves the predictive accuracy of GDA tools.
• The developed methods can enhance the usability and performance of genetic circuits in synthetic biology.
• This work provides a pathway to more robust and reliable synthetic biology designs through improved computational modeling.