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Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Global Regulatory Systems01:28

Global Regulatory Systems

Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
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...

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Related Experiment Video

Updated: Jul 6, 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

Gene Regulatory Network modelling: a state-space approach.

Fang-Xiang Wu1

  • 1Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. faw341@mail.usask.ca

International Journal of Data Mining and Bioinformatics
|April 11, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel state-space model to infer Gene Regulatory Networks (GRNs). The model effectively identifies gene interactions by analyzing gene expression data, revealing characteristics of real-world GRNs.

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Last Updated: Jul 6, 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

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene Regulatory Networks (GRNs) are crucial for understanding cellular processes.
  • Inferring GRNs from gene expression data is a complex challenge in systems biology.

Purpose of the Study:

  • To propose a novel state-space model with a control component for inferring Gene Regulatory Networks.
  • To enhance the accuracy and biological relevance of GRN inference.

Main Methods:

  • A state-space model is developed where genes are observation variables influenced by internal states and control variables.
  • The means of gene expression clusters are utilized as control variables for the internal state equation.
  • Bayesian Information Criterion (BIC) and Probabilistic Principal Component Analysis (PPCA) are employed for internal state estimation.

Main Results:

  • The proposed model was applied to two distinct gene expression datasets.
  • Inferred GRNs demonstrated characteristics consistent with known biological networks.
  • The model successfully captured key regulatory relationships within the datasets.

Conclusions:

  • The developed state-space model provides a robust framework for GRN inference.
  • The approach offers a promising tool for dissecting gene regulatory mechanisms.
  • Computational results validate the model's ability to infer biologically relevant GRNs.