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The Gene Regulatory Network (GRN) of biological cells governs a number of keyfunctionalities that enables them to adapt and survive through differentenvironmental conditions. Close observation of the GRN shows that the structureand operational principles resembles an Artificial Neural Network (ANN), whichcan pave the way for the development of Biological Artificial Intelligence. Inparticular, a gene's transcription and translation process resembles asigmoidal-like property based on transcription factor inputs. In this paper, wedevelop a mathematical model of gene-perceptron using a dual-layeredtranscription-translation chemical reaction model, enabling us to transform aGRN into a Gene Regulatory Neural Network (GRNN). We perform stability analysisfor each gene-perceptron within the fully-connected GRNN sub network todetermine temporal as well as stable concentration outputs that will result inreliable computing performance. We focus on a non-linear classifier applicationfor the GRNN, where we analyzed generic multi-layer GRNNs as well as E.ColiGRNN that is derived from trans-omic experimental data. Our analysis found thatvarying the parameters of the chemical reactions can allow us shift theboundaries of the classification region, laying the platform for programmableGRNNs that suit diverse application requirements.

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