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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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Protein Networks02:26

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Transient and Steady-state Response01:24

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In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
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What is Gene Expression?01:42

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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Related Experiment Video

Updated: Feb 10, 2026

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

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Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.

Anshi Gupta1, Ryeongkyung Yoon2, Krešimir Josić1,3,4

  • 1Department of Mathematics, University of Houston, Houston, TX 77204, USA.

Biorxiv : the Preprint Server for Biology
|February 9, 2026
PubMed
Summary

We developed a new Bayesian method to infer Gene Regulatory Network (GRN) structure using the Chemical Langevin Equation (CLE). This approach identifies regulatory interactions and kinetic parameters without needing transient data.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Gene Regulatory Networks (GRNs) are crucial for coordinating gene expression and biological system function.
  • Current GRN inference methods often overlook fundamental biochemical processes driving gene expression dynamics.
  • Understanding GRN architecture is vital for deciphering biological systems and developing targeted therapies.

Purpose of the Study:

  • To present a novel Bayesian inference approach for GRN structure identification.
  • To incorporate biochemical dynamics using the Chemical Langevin Equation (CLE) into GRN inference.
  • To improve the biological interpretability and structural identifiability of GRN inference methods.

Main Methods:

  • Developed a Bayesian inference framework utilizing the Chemical Langevin Equation (CLE) to model gene expression dynamics at stochastic equilibrium.
  • Employed a regularized horseshoe prior to handle the sparsity of GRN interactions, enabling selective shrinkage of unsupported interactions.
  • Validated the method using synthetic gene expression data with a known ground truth for benchmarking.

Main Results:

  • Successfully inferred kinetic parameters and identified network structure from gene expression data.
  • Demonstrated the ability to infer regulatory cycles without requiring observation of transient dynamics.
  • The Bayesian approach provided both biological interpretability and structural identifiability for GRN inference.

Conclusions:

  • The novel Bayesian CLE-based method offers a significant advancement in GRN inference.
  • This approach effectively models biochemical processes, enhancing the accuracy and reliability of inferred network structures.
  • The method provides a valuable alternative for researchers seeking interpretable and identifiable GRN models.