Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Mathematical Modeling: Problem Solving

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

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...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by

Same author

Accurate detection of tumor clonality and ongoing expansion mode from genomic data.

bioRxiv : the preprint server for biology·2026

Same author

Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model.

bioRxiv : the preprint server for biology·2026

Same author

Integrating EMT dynamics in model-based metastasis prediction.

Computer methods and programs in biomedicine·2026

Same author

BESTish: A Diffusion-Approximation Framework for Inferring Selection and Mutation in Clonal Hematopoiesis.

bioRxiv : the preprint server for biology·2026

Same author

Editorial: Mathematical modeling in discovery and analysis of immune responses.

Frontiers in immunology·2026

Same author

Statistical and Evolutionary Analysis of Sequenced DNA from Breast Cancer FFPE Specimens.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment

Published on: December 1, 2023

Mathematical modeling as a tool for planning anticancer therapy.

Andrzej Swierniak¹, Marek Kimmel, Jaroslaw Smieja

¹Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

European Journal of Pharmacology

|October 15, 2009

Summary

This summary is machine-generated.

This review explores mathematical models for optimizing cancer therapy, including chemotherapy, antiangiogenic treatments, and drug resistance strategies. These models offer potential for improved cancer treatment protocols.

More Related Videos

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Related Experiment Videos

Last Updated: Jun 19, 2026

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment

Published on: December 1, 2023

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Area of Science:

Mathematical Oncology
Control Theory
Systems Biology

Background:

Cancer therapy optimization is complex, requiring sophisticated approaches.
Mathematical modeling provides a framework for understanding and improving treatment strategies.
Existing models address chemotherapy, drug resistance, and angiogenesis.

Purpose of the Study:

To review mathematical models for cancer therapy optimization.
To cover key areas like phase-specific chemotherapy, antiangiogenic therapy, and drug resistance.
To discuss the biological basis and mathematical techniques in this field.

Main Methods:

Review of a large volume of literature on mathematical models.
Analysis of models ranging from simple cell cycle progression to complex resistance and signaling pathways.
Discussion of control theory, pharmacokinetic/pharmacodynamic (PK/PD) effects, and angiogenesis models.

Main Results:

Mathematical models offer explicit solutions for early-stage therapy optimization.
Complex models incorporate drug resistance, PK/PD effects, tumor angiogenesis, and molecular signaling.
The field has significant, though not fully realized, potential for cancer treatment advancement.

Conclusions:

Mathematical modeling is crucial for advancing cancer therapy optimization.
Integrating diverse biological aspects into models enhances their predictive power.
Further development and application of these models promise improved patient outcomes.