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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...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Jun 4, 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

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

Yoshinori Tamada1, Rui Yamaguchi, Seiya Imoto

  • 1Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. tamada@ims.u-tokyo.ac.jp

Bioinformatics (Oxford, England)
|February 15, 2011
PubMed
Summary

SiGN-SSM is new open-source software for estimating gene networks using state space models (SSM). It enables efficient analysis of gene expression profiles and identification of regulatory dependencies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • SiGN-SSM is an open-source software package for gene network estimation.
  • It utilizes state space models (SSM), which are statistical dynamic models.
  • The software is designed for analyzing short and/or replicated time series gene expression data.

Purpose of the Study:

  • To provide an efficient and parallelizable tool for gene network estimation.
  • To enable the analysis of temporal regulatory dependencies between genes.
  • To facilitate the extraction of differentially regulated genes from time series expression profiles.

Main Methods:

  • Estimation of state space models (SSM) for gene expression profiles.
  • Implementation of a novel parameter constraint to stabilize model estimation.
  • Utilizing supercomputers for statistical permutation tests to determine gene network structure.
  • Parallel processing capabilities for PCs and supercomputers.

Main Results:

  • SiGN-SSM provides a stable estimation of gene network models.
  • Gene network structure can be determined in a practical time using supercomputers.
  • The software is effective for analyzing temporal regulatory dependencies.
  • Differential gene regulation can be identified from time series expression data.

Conclusions:

  • SiGN-SSM is a powerful and versatile open-source software for gene network analysis.
  • Its parallel processing capabilities and novel constraints enhance efficiency and stability.
  • The software supports diverse applications in systems biology and bioinformatics research.