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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

277
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...
277
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
570
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
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...
249
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

541
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...
541
One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

One-Compartment Open Model: Urinary Excretion Data and Determination of k

631
The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
631
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Geppetto: a reusable modular open platform for exploring neuroscience data and models.

Matteo Cantarelli1,2,3, Boris Marin3,4, Adrian Quintana2,3,5

  • 1OpenWorm Foundation, USA matteo@openworm.org.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|September 12, 2018
PubMed
Summary

Geppetto is an open-source platform for neuroscience research, offering tools to visualize models and manage simulations. It supports applications like Open Source Brain and Virtual Fly Brain for data integration and analysis.

Keywords:
computational biologycomputational neurosciencedata visualizationmodelling and simulationneuroinformaticsscientific software

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Bioinformatics

Background:

  • Neuroscience research generates complex data and models requiring specialized visualization and simulation tools.
  • Existing tools often lack interoperability, hindering collaborative research and data sharing.
  • There is a need for a unified platform to manage diverse neuroscience data and simulation workflows.

Purpose of the Study:

  • To introduce Geppetto, an open-source middleware platform for building neuroscience visualization and simulation tools.
  • To demonstrate Geppetto's capability to integrate various neuroscience applications and data types.
  • To provide a flexible and accessible infrastructure for computational neuroscience research.

Main Methods:

  • Developed a domain-agnostic middleware platform (Geppetto) with a customizable frontend and backend.
  • Integrated Geppetto with existing neuroscience applications such as Open Source Brain, Virtual Fly Brain, NEURON-UI, and NetPyNE-UI.
  • Utilized Geppetto's components for model representation, data integration, visualization, and simulation management.

Main Results:

  • Geppetto successfully underpins multiple neuroscience applications, facilitating model visualization and simulation.
  • The platform enables integration of diverse data, including NeuroML models, Drosophila melanogaster anatomy, and imaging data.
  • Geppetto provides a unified approach to managing computational neuroscience models and simulations.

Conclusions:

  • Geppetto offers a robust, open-source solution for building and managing neuroscience research tools.
  • The platform enhances accessibility and integration within the computational neuroscience community.
  • Geppetto supports the advancement of neuroscience research by streamlining data visualization and simulation processes.