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

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...

Exponential Equations for Modeling Growth

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

You might also read

Related Articles

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

Sort by

Same author

Cancer Evolvability Determines Therapy Outcomes.

bioRxiv : the preprint server for biology·2026

Same author

Modeling VEGF and GLUT1 Expression as Coadapted Foraging Strategies in Cancer.

bioRxiv : the preprint server for biology·2026

Same author

Inverse game theory characterizes frequency-dependent selection driven by karyotypic diversity in triple-negative breast cancer.

PLoS computational biology·2026

Same author

Harnessing the extinction vortex against acute lymphoblastic leukemia.

Evolution, medicine, and public health·2026

Same author

Migrastatic therapy as a potential game-changer in adaptive cancer treatment.

Scientific reports·2026

Same author

Stackelberg Evolutionary Games of Cancer Treatment: What Treatment Strategy to Choose if Cancer Can be Stabilized?

Dynamic games and applications·2025

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Which evolutionary game-theoretic model best captures NSCLC dynamics?

Hasti Garjani¹, Johan Dubbeldam¹, Kateřina Staňková²

¹Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.

|June 1, 2026

Summary

This summary is machine-generated.

Mathematical models predict cancer dynamics by tracking drug-sensitive and resistant non-small cell lung cancer cells. Cancer-associated fibroblasts promote cell coexistence, while Alectinib drives competitive exclusion.

More Related Videos

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Area of Science:

Oncology
Mathematical Biology
Cancer Research

Background:

Predicting tumor growth and treatment response necessitates accurate mathematical models.
Eco-evolutionary dynamics in cancer involve interactions between sensitive and resistant cell populations.

Purpose of the Study:

To identify the best mathematical models for capturing non-small cell lung cancer (NSCLC) in-vitro dynamics.
To evaluate how environmental factors like Alectinib and cancer-associated fibroblasts (CAFs) influence tumor cell interactions.

Main Methods:

Fitting a family of two-population models to in-vitro NSCLC data under varying conditions (with/without Alectinib and CAFs).
Comparing logistic, Gompertz, and von Bertalanffy growth models with Norton-Simon, linear, and ratio-dependent drug efficacy terms.
Incorporating density dependence, frequency-dependent competition, and drug response into models for mechanistic interpretation.

Main Results:

The logistic growth model with ratio-dependent drug efficacy provided the best fit for monoculture data.
Growth rate and carrying capacity remained stable across different CAF conditions.
CAFs promoted coexistence of drug-sensitive and resistant cells, while Alectinib led to competitive exclusion.

Conclusions:

Model selection requires evaluating both statistical fit and biological plausibility for therapeutic applications.
Environmental factors significantly alter competitive dynamics and drug response in NSCLC.
Understanding eco-evolutionary dynamics is crucial for developing effective cancer therapies.