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Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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Related Experiment Video

Updated: Jun 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

A Markov-blanket-based model for gene regulatory network inference.

Ramesh Ram1, Madhu Chetty

  • 1Gippsland School of Information Technology, Monash University, Gippsland Campus, VIC 3842, Australia. ramesh.ram@infotech.monash.edu.au

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|January 15, 2011
PubMed
Summary

This study introduces an efficient two-step Markov blanket method for accurately inferring gene regulatory networks (GRNs) from gene expression data. The approach demonstrates high accuracy and robustness, even with noisy data, for biological network modeling.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Last Updated: Jun 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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Published on: December 7, 2021

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
  • Inferring GRNs from large-scale gene expression data is challenging due to complexity and noise.
  • Existing methods often struggle with accuracy and scalability.

Purpose of the Study:

  • To present an efficient two-step Markov blanket (MB) method for modeling and inferring complex GRNs.
  • To develop a robust approach for analyzing time-series gene expression data.
  • To validate the method's accuracy and biological relevance using synthetic and yeast cell cycle data.

Main Methods:

  • Discovery of a gene's Markov Blanket (MB).
  • Formulation of a flexible network quality measure.
  • Efficient network searching using a guided genetic algorithm.
  • Pruning to obtain a minimal set of correct gene interactions.

Main Results:

  • The proposed method demonstrates excellent inferential capabilities and high accuracy, even with noisy data.
  • Robustness validated across synthetic datasets with varying parameters (topology, sample size, noise, etc.).
  • Inferred yeast cell cycle GRN shows biological relevance and predicts novel interactions.

Conclusions:

  • The two-step Markov blanket method provides an efficient and accurate approach for GRN inference.
  • The method is robust to various data complexities, including noise and hidden nodes.
  • The inferred networks offer biological insights and potential for discovering new gene interactions.