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Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network.

Adrian Ratwatte¹, Samitha Somathilaka², Sasitharan Balasubramaniam¹

¹School of Computing, University of Nebraska-Lincoln, 104 Schorr Center, Lincoln, Nebraska, USA.

Biophysical Reports

|June 7, 2024

Summary

This summary is machine-generated.

Biological gene regulatory networks (GRNs) can function as artificial neural networks (ANNs), enabling novel wet-neuromorphic computing. This study develops three nonlinear classifiers using gene regulatory neural networks (GRNNs) for molecular machine learning.

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Cellular gene regulatory networks (GRNs) exhibit structural and operational similarities to artificial neural networks (ANNs).
This resemblance offers potential for developing wet-neuromorphic computing systems.
Genes can be conceptualized as gene-perceptrons, processing transcription factor inputs to produce protein outputs.

Purpose of the Study:

To establish nonlinear classifiers for molecular machine learning by leveraging the inherent sigmoidal nonlinearity of gene expression.
To analyze the temporal stability of gene regulatory neural networks (GRNNs) for reliable computational performance.
To develop and evaluate GRNN-based classifiers for diverse applications.

Main Methods:

Modeling gene regulatory networks (GRNs) as gene regulatory neural networks (GRNNs) with weighted inputs and nonlinear activation functions.
Employing eigenvalue-based stability analysis to determine maximum stable concentration levels and minimize errors.
Utilizing Lyapunov stability theorem for temporal stability analysis of dynamic GRNNs.
Developing three nonlinear classifiers using two generic multilayer sub-GRNNs and one sub-GRNN from Escherichia coli.

Main Results:

Successful establishment of nonlinear classifiers based on GRNNs, demonstrating the feasibility of molecular machine learning.
Stability analyses confirmed maximum concentration levels, crucial for minimizing fluctuations and computational errors in GRNNs.
Developed classifiers showed adaptability to different application requirements, highlighting the versatility of the GRNN approach.

Conclusions:

The GRN-to-GRNN mapping provides a robust framework for developing bio-inspired computing systems.
Stability analysis is essential for ensuring the reliable operation of GRNN-based classifiers.
The developed GRNN classifiers demonstrate potential for diverse molecular machine learning applications.