Stability Analysis of Non-Linear Classifiers using Gene Regulatory
  Neural Network for Biological AI
Adrian Ratwatte Samitha Somathilaka Sasitharan Balasubramaniam Assaf A. Gilad
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.