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Neural Regulation

Neural Regulation

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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Related Experiment Video

Updated: Jul 5, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling gene regulatory networks using neural network architectures.

Hantao Shu¹, Jingtian Zhou^2,3, Qiuyu Lian^4,5

¹Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.

Nature Computational Science

|January 13, 2024

Summary

DeepSEM, a novel deep generative model, infers gene regulatory networks (GRNs) and single-cell RNA sequencing (scRNA-seq) data representations. This method accurately models gene regulation and enhances scRNA-seq analysis for biological discovery.

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Last Updated: Jul 5, 2025

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Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Gene regulatory networks (GRNs) are crucial for understanding cell identity.
Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data for cellular analysis.
Existing methods for GRN inference and scRNA-seq analysis have limitations.

Purpose of the Study:

To develop a deep generative model, DeepSEM, for joint inference of GRNs and scRNA-seq data representations.
To improve the accuracy and efficiency of GRN inference and scRNA-seq data analysis.
To provide a robust tool for exploring complex molecular interactions in single cells.

Main Methods:

Developed DeepSEM, a deep generative model incorporating a neural network-based structural equation model (SEM).
Utilized scRNA-seq data to jointly infer GRNs and learn meaningful data representations.
Benchmarked DeepSEM against state-of-the-art methods on various computational tasks.

Main Results:

DeepSEM demonstrated comparable or superior performance in GRN inference, scRNA-seq visualization, clustering, and simulation.
Predicted gene regulations for mouse cortex cell-type marker genes were validated using epigenetic data.
The model effectively captures complex regulatory relationships among genes.

Conclusions:

DeepSEM offers an accurate and efficient approach for analyzing scRNA-seq data and inferring GRNs.
The method provides a powerful tool for advancing our understanding of cellular mechanisms.
DeepSEM facilitates the integration of diverse biological data for comprehensive analysis.