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Multiscale modeling in disease.

Ashlee N Ford Versypt1

  • 1Department of Chemical and Biological Engineering and Institute for Computational and Data Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.

Current Opinion in Systems Biology
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

Multiscale computational modeling integrates molecular, cellular, and tissue interactions to understand complex biological systems. This approach aids in studying diseases like fibrosis, cancer, and respiratory infections.

Keywords:
Agent-based modelingComputational modelingExtracellular matrixFibrosisInflammationMetastasisMultiscale modelingTissue growthTissue remodeling

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Area of Science:

  • Computational biology
  • Systems biology
  • Biophysics

Background:

  • Biological systems involve complex interactions across multiple length and time scales, from molecular signaling to whole-body physiology.
  • Understanding these emergent phenomena is crucial for deciphering disease mechanisms.
  • Existing models often focus on single scales, limiting comprehensive analysis.

Purpose of the Study:

  • To highlight recent applications of multiscale computational modeling.
  • To demonstrate the integration of intracellular, cellular, and tissue levels in modeling.
  • To showcase the role of biochemical and biomechanical factors in disease.

Main Methods:

  • Developing and applying multiscale computational models.
  • Integrating molecular signaling, cell-cell interactions, and tissue-level dynamics.
  • Incorporating biochemical and biomechanical modulations.

Main Results:

  • Multiscale models successfully connect effects across different scales, from intracellular to whole-body.
  • These models illuminate disease mechanisms in conditions such as fibrosis, joint/bone diseases, respiratory infections, and cancers.
  • Biochemical and biomechanical factors are shown to be critical in disease pathogenesis.

Conclusions:

  • Multiscale computational modeling is a powerful approach for understanding complex biological systems and disease.
  • Integrating multiple scales provides a more holistic view of disease mechanisms.
  • This modeling paradigm has significant implications for disease research and therapeutic development.