Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.3K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

423
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
423
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

438
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
438
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

298
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...
298
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

3.3K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
3.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

297
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...
297

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How to prepare and deliver a great talk.

The FEBS journal·2019
Same author

How to design an outstanding poster.

The FEBS journal·2018
Same author

Writing a successful fellowship or grant application.

The FEBS journal·2017
Same author

Scientific career paths - part II.

The FEBS journal·2017
Same author

Variable repeats in the eukaryotic polyubiquitin gene ubi4 modulate proteostasis and stress survival.

Nature communications·2017
Same author

Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins.

Nature structural & molecular biology·2017
Same journal

The effects of two Leu-to-Pro substitutions, L57P and L43P, on structural and functional properties of cardiac tropomyosin.

The FEBS journal·2026
Same journal

Stimulating proteasomal degradation in human proteinopathies.

The FEBS journal·2026
Same journal

A lipid-sensitive food choice behavior influences aging outcomes from a longevity-promoting diet.

The FEBS journal·2026
Same journal

The interaction network of a rice seed-specific transcription factor OsMADS29 and the calcium sensors, calmodulin, and calmodulin-like proteins.

The FEBS journal·2026
Same journal

A large family of unusual voltage-sensing proton channels (Hv3) in mollusks.

The FEBS journal·2026
Same journal

RVB-1 and RVB-2 are stress responsive proteins in Neurospora crassa and RVB-1 interacts with the centromeric Shugoshin (SGO-1) protein.

The FEBS journal·2026
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.3K

Malaria, metabolism and mathematical models.

Rita Gemayel1

  • 1The FEBS Journal, Editorial Office, Cambridge, UK.

The FEBS Journal
|August 24, 2017
PubMed
Summary
This summary is machine-generated.

This commentary discusses mathematical modeling of Plasmodium falciparum glycolysis and identifies new drug targets for malaria treatment. Research highlights in vivo validation of metabolic targets for drug development.

More Related Videos

Author Spotlight: Identifying Compensatory Pathways in Malaria Parasites Containing Hypomorphic Allele of Essential Protein Kinases
09:13

Author Spotlight: Identifying Compensatory Pathways in Malaria Parasites Containing Hypomorphic Allele of Essential Protein Kinases

Published on: November 22, 2024

1.9K
Protocol for Production of a Genetic Cross of the Rodent Malaria Parasites
13:39

Protocol for Production of a Genetic Cross of the Rodent Malaria Parasites

Published on: January 3, 2011

15.7K

Related Experiment Videos

Last Updated: Feb 24, 2026

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei
09:02

An Experimental Model to Study Tuberculosis-Malaria Coinfection upon Natural Transmission of Mycobacterium tuberculosis and Plasmodium berghei

Published on: February 17, 2014

20.3K
Author Spotlight: Identifying Compensatory Pathways in Malaria Parasites Containing Hypomorphic Allele of Essential Protein Kinases
09:13

Author Spotlight: Identifying Compensatory Pathways in Malaria Parasites Containing Hypomorphic Allele of Essential Protein Kinases

Published on: November 22, 2024

1.9K
Protocol for Production of a Genetic Cross of the Rodent Malaria Parasites
13:39

Protocol for Production of a Genetic Cross of the Rodent Malaria Parasites

Published on: January 3, 2011

15.7K

Area of Science:

  • Biochemistry
  • Parasitology
  • Drug Discovery

Background:

  • The metabolism of Plasmodium falciparum, the parasite causing malaria, is crucial for its survival and proliferation during infection.
  • Identifying metabolic vulnerabilities in P. falciparum is a key strategy for developing novel antimalarial drugs.
  • Recent research has focused on understanding the intricacies of parasite metabolism to uncover new therapeutic targets.

Purpose of the Study:

  • To review recent studies on the mathematical modeling of glycolysis in Plasmodium falciparum.
  • To highlight the identification and in vivo validation of metabolic drug targets for malaria.
  • To provide insights into the dynamic behavior of P. falciparum metabolism during infection.

Main Methods:

  • Mathematical modeling of Plasmodium falciparum glycolysis.
  • In silico analysis of metabolic pathways.
  • In vivo validation of identified metabolic drug targets.

Main Results:

  • Recent studies have advanced the mathematical modeling of P. falciparum glycolysis.
  • Several potential metabolic drug targets have been identified through computational and experimental approaches.
  • The in vivo efficacy of targeting specific metabolic pathways in P. falciparum has been demonstrated.

Conclusions:

  • Understanding Plasmodium falciparum metabolism is vital for effective malaria drug development.
  • Mathematical modeling and target validation are powerful tools for identifying new antimalarial strategies.
  • Further research into parasite metabolism holds promise for overcoming drug resistance and improving treatment outcomes.