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Related Experiment Video

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A fully coupled space-time multiscale modeling framework for predicting tumor growth.

Mohammad Mamunur Rahman1, Yusheng Feng1, Thomas E Yankeelov2,3,4,5

  • 1Center for Simulation, Visualization and Real-Time Prediction, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, United States.

Computer Methods in Applied Mechanics and Engineering
|November 22, 2017
PubMed
Summary
This summary is machine-generated.

This study presents a multiscale computational framework to predict human tumor growth by integrating tissue, cellular, and subcellular biological events. The model accurately forecasts tumor characteristics and treatment responses, advancing computational biomechanics.

Keywords:
Bridging scale algorithmCancer modelingContinuum mixture theoryNetwork modelingSignaling transduction pathwayTreatment outcome prediction

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Area of Science:

  • Computational Biomechanics
  • Mathematical Modeling
  • Cancer Research

Background:

  • Biological systems are complex, multi-component structures with behavior varying across spatial and temporal scales.
  • Modeling multiscale events, especially human tumor growth, presents significant challenges in computational biomechanics.
  • Bridging diverse scales (10⁻³–10³ mm, 10⁻⁶–10⁷ s) is crucial for accurate predictive models.

Purpose of the Study:

  • To develop a general, fully coupled space-time multiscale framework for predicting tumor growth.
  • To integrate biological events across tissue, cellular, and subcellular levels into a unified model.
  • To create a biologically-driven, volumetrically-consistent, and biophysically-sound tumor growth model.

Main Methods:

  • Developed a multiscale framework comprising tissue, cellular, and subcellular models.
  • Employed partial differential equations for tissue growth, agent-based modeling for cellular activities, and ordinary differential equations for signaling pathways.
  • Calibrated the model using experimental observations to ensure accuracy.

Main Results:

  • The model successfully predicted key tumor growth characteristics, including morphological instability and varied cell density patterns.
  • It accurately simulated the impact of drug delivery on tumor growth rates and cellular proliferation.
  • Demonstrated the framework's capability through several 3D applications of tumor growth and associated cellular/subcellular events.

Conclusions:

  • The developed multiscale framework provides a robust tool for predicting tumor growth and treatment outcomes.
  • The integration of different biological scales enhances the predictive power of computational biomechanics models.
  • This approach offers a pathway for more accurate and biologically relevant simulations in cancer research.