DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron.

Area of Science:

• Synthetic Biology
• Molecular Biology
• Biochemistry

Background:

• Molecular pattern recognition is crucial for organism survival, enabling environmental sensing and response.
• Previous synthetic gene classifiers primarily utilized DNA strand-displacement reactions.
• A need exists for novel synthetic gene-based classification systems.

Purpose of the Study:

• To develop a synthetic in vitro transcription and translation (TXTL)-based perceptron.
• To implement a weighted sum operation (WSO) coupled to a thresholding function for binary classification.
• To demonstrate a genetically implemented system for molecular pattern recognition.

Main Methods:

• Constructed a TXTL-based WSO circuit using toehold switch riboregulators.
• Converted DNA inputs into a Green Fluorescent Protein (GFP) output, correlating concentration with input patterns and weights.
• Replaced GFP with the *Escherichia coli* σ28-factor and introduced a σ28 inhibitor for thresholding the WSO output.

Main Results:

• Demonstrated a TXTL-based WSO circuit converting DNA inputs to a GFP output whose concentration reflected input patterns.
• Successfully coupled the WSO output to a downstream reporter network using the σ28-factor.
• Achieved binary classification, with reporter protein expression only when the σ28 output exceeded the inhibition threshold.

Conclusions:

• Presented a novel synthetic gene-based perceptron utilizing TXTL and toehold switches.
• Established a modular WSO circuit adaptable for different output proteins and downstream networks.
• Demonstrated a genetically implemented binary classifier capable of recognizing specific input patterns based on a threshold.