Performance Prediction of Fundamental Transcriptional Programs

Area of Science:

• Synthetic biology
• Genetic engineering
• Computational biology

Background:

• Transcriptional programming uses engineered transcription factors for cellular decision-making (e.g., Boolean logic).
• Increasing complexity of biological circuits makes exhaustive experimental evaluation impractical.
• A predictive tool is needed to guide and accelerate the design of transcriptional programs.

Purpose of the Study:

• To develop and experimentally characterize a collection of network-capable single-input logical operations.
• To use this data to model and predict the performance of more complex two-input logical operations.
• To establish a foundation for the predictive design of increasingly complex transcriptional programs.

Main Methods:

• Development and experimental characterization of engineered BUFFER (repressor) and NOT (antirepressor) logical operations.
• Utilized developed metrology to model and predict the performance of compressed two-input AND and NOR gates.
• Extended modeling to predict performance of compressed mixed phenotype logical operations (A NIMPLY B and B NIMPLY A gates).

Main Results:

• Successfully developed and characterized a library of single-input logical operations.
• Accurately modeled and predicted the performance of fundamental two-input compressed logical operations.
• Demonstrated that single-input data is sufficient for predicting complex circuit performance.

Conclusions:

• Single-input data provides a robust foundation for predicting the behavior of complex genetic logic circuits.
• The developed metrology and predictive models accelerate the design of synthetic gene networks.
• This work enables the predictive design of transcriptional programs with significantly greater complexity.