# Stability Analysis of Non-Linear Classifiers using Gene Regulatory Neural Network for Biological AI

arXiv: https://arxiv.org/abs/2310.04424 · HTML: https://mlsanity.com/paper/2310.04424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Internet of Everything (IoE) -- From Molecules to the Universe](https://mlsanity.com/paper/2301.03374/) (2022)
- [Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges](https://mlsanity.com/paper/2212.11910/) (2022)

## Abstract (arXiv)

The Gene Regulatory Network (GRN) of biological cells governs a number of key functionalities that enables them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resembles an Artificial Neural Network (ANN), which can pave the way for the development of Biological Artificial Intelligence. In particular, a gene's transcription and translation process resembles a sigmoidal-like property based on transcription factor inputs. In this paper, we develop a mathematical model of gene-perceptron using a dual-layered transcription-translation chemical reaction model, enabling us to transform a GRN into a Gene Regulatory Neural Network (GRNN). We perform stability analysis for each gene-perceptron within the fully-connected GRNN sub network to determine temporal as well as stable concentration outputs that will result in reliable computing performance. We focus on a non-linear classifier application for the GRNN, where we analyzed generic multi-layer GRNNs as well as E.Coli GRNN that is derived from trans-omic experimental data. Our analysis found that varying the parameters of the chemical reactions can allow us shift the boundaries of the classification region, laying the platform for programmable GRNNs that suit diverse application requirements.