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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.

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

Updated: Aug 7, 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

Biologically informed NeuralODEs for genome-wide regulatory dynamics.

Intekhab Hossain, Viola Fanfani, Jonas Fischer

Biorxiv : the Preprint Server for Biology

|March 13, 2023

Summary

This summary is machine-generated.

PHOENIX, a novel neural ODE framework, infers gene regulatory network dynamics for better disease insights. It scales to whole-genome analysis, offering biologically interpretable results for intervention optimization.

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Last Updated: Aug 7, 2025

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Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Gene regulatory networks (GRNs) are crucial for understanding biological processes.
Ordinary differential equations (ODEs) model GRN dynamics but face scalability and interpretability challenges.
Existing ODE inference methods are limited to small networks or require restrictive assumptions.

Conclusions:

PHOENIX offers a powerful approach for modeling complex gene regulatory networks.
The framework advances the understanding of disease mechanisms and therapeutic responses.
PHOENIX facilitates the optimization of interventions through accurate dynamic modeling.