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Related Concept Videos

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...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
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...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

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Related Experiment Video

Updated: May 24, 2026

gP2S, an Information Management System for CryoEM Experiments
13:01

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

OMERO: flexible, model-driven data management for experimental biology.

Chris Allan1, Jean-Marie Burel, Josh Moore

  • 1Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee, Scotland, UK.

Nature Methods
|March 1, 2012
PubMed
Summary

OME Remote Objects (OMERO) is an open-source software platform designed for managing diverse biological datasets. It provides a unified interface for accessing and utilizing various data types, supporting numerous research applications.

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Published on: November 22, 2019

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Last Updated: May 24, 2026

gP2S, an Information Management System for CryoEM Experiments
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gP2S, an Information Management System for CryoEM Experiments

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Area of Science:

  • Bioinformatics
  • Data Management
  • Scientific Software Development

Background:

  • Data-intensive research requires robust tools for managing complex, multidimensional, and heterogeneous biological datasets.
  • Existing platforms may lack the flexibility to handle diverse data types from various microscopy and screening techniques.

Purpose of the Study:

  • To introduce OME Remote Objects (OMERO), a novel software platform.
  • To provide a unified interface for accessing and utilizing a wide range of biological data.
  • To demonstrate the platform's flexibility across different research applications.

Main Methods:

  • Development of a server-based middleware application.
  • Implementation of a unified interface for images, matrices, and tables.
  • Ensuring open-source availability and community support.

Main Results:

  • OMERO successfully integrates and manages multidimensional, heterogeneous biological datasets.
  • The platform supports diverse data types including images, matrices, and tables.
  • OMERO has been successfully applied to light-microscopy, high-content screening, electron-microscopy, and genotype data.

Conclusions:

  • OMERO offers a flexible and unified solution for managing complex biological data.
  • The open-source nature of OMERO promotes its widespread adoption and development in data-intensive research.
  • OMERO enhances the accessibility and usability of biological data for scientific discovery.