Design principles of neuromorphic computing using genetic circuits

Cells have evolved to sense a wide range of input combinations and integrate those signals through signaling pathways to produce context-specific responses, such as differentiation, cell-type specification, and patterning. To replicate this information-processing capacity, synthetic biology has developed large-scale circuitry inspired by the fundamental principles of computer science. Within this framework, neuromorphic computing implemented using genetic circuits offers the opportunity to significantly enhance the computational capabilities of single cells. In this work, we establish design principles for implementing neuromorphic computing in living cells by identifying the key feature that enables a chemical reaction network to function as a perceptron: an input-output mapping with a tunable threshold. We demonstrate that four ubiquitous chemical reaction networks, namely molecular sequestration, catalytic degradation, competitive binding, and activation/deactivation cycles, all satisfy this requirement and can be engineered as perceptrons. By layering these perceptrons into multi-layer architectures, we then show how to construct both linear and nonlinear decision boundaries through rational tuning of production rates that encode network weights. As proof of principle, we apply this framework to design neural networks capable of discriminating between healthy and cancer cells based on gene expression data from 19 tissue types. Together, this work formalizes the design principles for engineering genetic circuits as neural networks and establishes a foundation for implementing next-generation cellular computation.