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

TL;DR

This paper models gene regulatory networks as neural networks, performs stability analysis on their non-linear classifiers, and demonstrates how parameter tuning can customize classification boundaries for biological AI applications.

Contribution

It introduces a mathematical gene-perceptron model transforming GRNs into neural networks and analyzes their stability for reliable classification performance.

Findings

Parameter variation shifts classification boundaries

Stable concentration outputs enable reliable computing

GRNNs can be tuned for diverse applications

Abstract

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.