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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...
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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...

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Related Experiment Video

Updated: Jun 23, 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

Predicting differences in gene regulatory systems by state space models.

Rui Yamaguchi1, Seiya Imoto, Mai Yamauchi

  • 1Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. ruiy@ims.u-tokyo.ac.jp

Genome Informatics. International Conference on Genome Informatics
|May 9, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method using state space models to identify key gene expression differences between cell samples. The approach successfully detects potential drug targets by finding unpredictable gene patterns.

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Last Updated: Jun 23, 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

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
  • Genomics

Background:

  • Gene expression analysis often faces challenges in distinguishing true regulatory differences from noise.
  • Time-course gene expression data provides dynamic insights into cellular processes.
  • Identifying specific drug targets requires understanding alterations in gene regulatory systems.

Purpose of the Study:

  • To develop a statistical strategy for predicting differentially regulated genes in time-course expression data.
  • To differentiate genes with genuinely altered regulation from those with similar patterns driven by conserved mechanisms.
  • To identify candidate biomarkers and drug target genes by detecting unpredictable expression patterns.

Main Methods:

  • Inferred gene regulatory system from control samples using a state space model.
  • Utilized the control model to predict gene expression patterns in case samples.
  • Assessed the significance of differences between predicted and actual case data to identify unpredictable genes.

Main Results:

  • Successfully applied the strategy to human small airway epithelial cells treated with gefitinib.
  • Identified unpredictable genes as candidates for specific gefitinib targets.
  • Highlighted differences in regulatory systems for these unpredictable genes.

Conclusions:

  • The proposed statistical strategy is a promising tool for identifying key differences in gene regulatory systems.
  • This method can effectively detect novel biomarkers and potential drug target genes.
  • The approach aids in understanding drug-specific effects on cellular regulation.