Neuromorphic Circuits

Part 1: Intracellular Artificial Neural Networks

1. Advantages of IANNs vs traditional genetic circuits

Traditional genetic circuits usually behave like Boolean logic systems (ON/OFF), meaning they respond in discrete states (e.g., gene expressed or not). In contrast, IANNs offer several key advantages:

Criteria	Description
Graded responses instead of binary outputs	IANNs can process inputs in a continuous manner (like real neural networks). This allows more nuanced control of gene expression
Integration of multiple inputs simultaneously	Instead of simple AND/OR logic, IANNs can weigh inputs differently (e.g., X1 contributes more than X2)
Higher computational complexity	They can approximate nonlinear functions and make more sophisticated “decisions” inside cells
Scalability	Multilayer architectures allow hierarchical information processing, similar to deep learning
Better noise tolerance	Weighted systems can be more robust to biological variability compared to strict Boolean thresholds

(Nilsson et al., 2022; Müller et al., 2025)

2. Application of an IANN

For example, it could be a “Smart infection-detection system,” where the goal is to engineer a cell that detects early-stage infection and produces a therapeutic or reporter signal.

How it would work?

Inputs:

X1: Presence of bacterial quorum sensing molecules (e.g., AHLs)

X2: Host inflammation marker (e.g., ROS levels)

X3: pH changes (acidic microenvironment)

Processing (IANN behavior):

Each input is weighted differently

The network integrates signals:

High AHL + moderate ROS → strong activation

Low AHL + high ROS → weak activation

Uses a threshold function to decide output intensity

Output:

Expression of:

Fluorescent protein (diagnostic)

OR antimicrobial peptide (therapeutic)

There are some limitations in the application process, for example:

Noise in gene expression
Difficult tuning of weights (promoter strength, RBS, degradation rates)