Metabolic perceptrons for neural computing in biological systems

Area of Science:

• Synthetic biology
• Metabolic engineering
• Computational biology

Background:

• Current synthetic biological circuits primarily use gene expression for digital logic-based information processing.
• There is a need for alternative biological computation strategies for diverse applications.

Purpose of the Study:

• To introduce a novel approach for biological computation using computer-aided designed metabolic circuits.
• To demonstrate the implementation of analog and weighted adders, and perceptrons in both whole-cell and cell-free systems.

Main Methods:

• Development of metabolic transducers to create analog adders for metabolite concentration summation.
• Construction of weighted adders allowing adjustable contributions of different metabolites.
• Implementation of four-input perceptrons using computational models and predicted weights for binary classification of metabolite combinations.

Main Results:

• Successful construction of analog and weighted adder circuits using metabolic transducers.
• Demonstration of two four-input perceptrons capable of binary classification of metabolite combinations.
• Validation of computational models fitted on experimental data for predicting circuit behavior.

Conclusions:

• Metabolic circuits offer a new paradigm for biological computation, distinct from gene expression-based systems.
• The developed perceptron-mediated neural computing provides a foundation for scalable multiplex sensing.
• This approach enables rapid and advanced detection of metabolite combinations for various applications.