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Related Concept Videos

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

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...
Cancer Survival Analysis01:21

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...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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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.
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Related Experiment Video

Updated: Jul 13, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.

José Alberto Rodrigues1, João Serra Lopes2

  • 1CIMA and Mathematics Department of Instituto Superior de Engenharia de Lisboa, Polytechnic University of Lisbon, Lisbon, Portugal. jose.rodrigues@isel.pt.

Advances in Experimental Medicine and Biology
|July 11, 2026
PubMed
Summary

Mathematical modeling of cancer metabolism reveals tumor cell plasticity and identifies vulnerabilities. These computational approaches aid in developing personalized, metabolism-based cancer therapies for precision oncology.

Keywords:
Agent-based modelsCancer metabolismFlux balance analysisGenome-scale metabolic modelsKinetic modelsMachine learningMathematical modelingMetabolism-based therapyMulti-omics integrationPrecision oncology

Related Experiment Videos

Last Updated: Jul 13, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

Area of Science:

  • Oncology
  • Systems Biology
  • Computational Biology

Background:

  • Cancer cells exhibit metabolic plasticity, adapting pathways like glycolysis and oxidative phosphorylation for survival and therapy resistance.
  • The Warburg effect is a known hallmark, but dynamic metabolic engagement is crucial for tumor progression.
  • Tumor metabolic adaptations create exploitable vulnerabilities for therapeutic intervention.

Purpose of the Study:

  • To explore the role of mathematical and computational modeling in understanding cancer metabolism.
  • To highlight how these models can translate biological insights into clinical applications for cancer therapy.
  • To emphasize the integration of modeling approaches for precision oncology.

Main Methods:

  • Utilizing kinetic models for mechanistic flux control analysis.
  • Employing constraint-based frameworks like flux balance analysis for genome-scale predictions.
  • Integrating agent-based models for spatial heterogeneity and tumor-immune interactions.
  • Applying machine learning and hybrid data-driven approaches with multi-omics data.
  • Developing personalized genome-scale metabolic models from patient-specific data.
  • Using hybrid frameworks like physics-informed neural networks and neural ordinary differential equations.

Main Results:

  • Mathematical modeling unravels the complexity of cancer cell metabolism and identifies vulnerabilities.
  • Constraint-based and agent-based models predict metabolic liabilities and tumor microenvironment dynamics.
  • Machine learning and personalized models predict therapeutic response and stratify patients.
  • Hybrid models enhance predictive capacity for tumor-immune-metabolism dynamics.
  • These approaches facilitate virtual hypothesis testing, biomarker discovery, and adaptive therapy design.

Conclusions:

  • Mathematical and computational modeling are powerful tools for understanding and targeting cancer metabolism.
  • Personalized metabolic models can guide the selection of effective, metabolism-based therapies.
  • Integrating mechanistic insights with predictive modeling is key to advancing precision oncology.
  • These frameworks accelerate the development of personalized cancer treatments by identifying metabolic targets and improving patient stratification.