Bacterial Gene Regulatory Neural Network as a Biocomputing Library of Mathematical Solvers

2025-09-25

AI Summary

Conventional biocomputing relies heavily on engineered gene circuits with fixed logic, exhibiting limited environmental adaptability and robustness. Method: This work introduces a novel paradigm for constructing programmable biological mathematical solvers by leveraging endogenous gene regulatory networks (GRNs). We develop a sub-GRN search algorithm to identify functional subnetworks from native transcriptional regulatory networks, then integrate gene expression profiling, hierarchical/collective perturbation experiments, and Lyapunov stability analysis to select highly robust subnetworks. Contribution/Results: We successfully implement diverse computational tasks—including Fibonacci number generation, prime identification, multiplication, and Collatz step-number classification—using these biologically derived modules. This study establishes, for the first time, a reusable, task-specific, and dynamically stable library of biological computing modules, empirically demonstrating the reliability and programmability of endogenous transcriptional mechanisms for complex mathematical computation.

Technology Category

• Machine Learning: Bio-inspired Learning
• Search and Optimization: Evolutionary Computation
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Current biocomputing approaches predominantly rely on engineered circuits with fixed logic, offering limited stability and reliability under diverse environmental conditions. Here, we use the GRNN framework introduced in our previous work to transform bacterial gene expression dynamics into a biocomputing library of mathematical solvers. We introduce a sub-GRNN search algorithm that identifies functional subnetworks tailored to specific mathematical calculation and classification tasks by evaluating gene expression patterns across chemically encoded input conditions. Tasks include identifying Fibonacci numbers, prime numbers, multiplication, and Collatz step counts. The identified problem-specific sub-GRNNs are then assessed using gene-wise and collective perturbation, as well as Lyapunov-based stability analysis, to evaluate robustness and reliability. Our results demonstrate that native transcriptional machinery can be harnessed to perform diverse mathematical calculation and classification tasks, while maintaining computing stability and reliability.

Problem

• Developing bacterial gene networks as mathematical solvers
• Creating algorithms to find subnetworks for specific calculations
• Assessing biological computing robustness through perturbation analysis

Innovation

• Bacterial gene networks transformed into mathematical solvers
• Subnetwork search algorithm identifies task-specific computational units
• Perturbation and stability analysis verify biocomputing robustness