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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Time Course of Drug Effect01:14

Time Course of Drug Effect

The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50 percent of...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
The Integrated Rate Law: The Dependence of Concentration on Time02:39

The Integrated Rate Law: The Dependence of Concentration on Time

While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

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Updated: May 11, 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

Published on: December 7, 2021

Inference of dynamic networks using time-course data.

Yongsoo Kim1, Seungmin Han, Seungjin Choi

  • 1POSTECH, Pohang, 790-784, Republic of Korea. Tel.: 82-54-279-2393; Fax: 82-54-279-8409; dhhwang@postech.ac.kr.

Briefings in Bioinformatics
|May 24, 2013
PubMed
Summary
This summary is machine-generated.

This review covers computational methods for analyzing dynamic biological networks. It highlights challenges in tracking network changes and explores techniques for inferring both temporal and spatial transitions in cellular processes.

Keywords:
dynamic networknetwork inferencespatiotemporal dynamics

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Area of Science:

  • Systems biology and computational biology
  • Molecular and cellular biology

Background:

  • Cells function via dynamic biological networks, involving changes in molecular components (nodes) and interactions (edges) over time.
  • Global genomic and proteomic technologies offer insights but struggle to pinpoint temporal shifts in network structures.
  • Identifying dynamic changes in biological networks is crucial for understanding cellular functions.

Purpose of the Study:

  • To review computational methods for inferring dynamic biological networks.
  • To summarize approaches for estimating the temporal and spatial transitions within these networks.
  • To provide an overview of current techniques for analyzing dynamic network characteristics.

Main Methods:

  • Review of existing literature on computational methods for dynamic network inference.
  • Analysis of techniques for estimating topological and functional properties of dynamic networks.
  • Exploration of methods for assessing spatial transitions in biological networks.

Main Results:

  • Several computational approaches have been developed to address the challenge of identifying temporal node and edge transitions in biological networks.
  • These methods enable the estimation of dynamic topological and functional characteristics.
  • The review also covers methods for inferring spatial transitions in biological networks.

Conclusions:

  • Computational methods are essential for decoding the dynamic nature of biological networks.
  • Further development and application of these methods are needed to fully understand cellular dynamics.
  • This review consolidates current knowledge on inferring dynamic and spatial network transitions.