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Multiscale modeling and data integration in the virtual physiological rat project.

Daniel A Beard1, Maxwell L Neal, Nazanin Tabesh-Saleki

  • 1Biotechnology and Bioengineering Center and Center for Computational Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.

Annals of Biomedical Engineering
|July 19, 2012
PubMed
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Integrating multiple computational models across physiological scales is crucial for understanding complex diseases. The Virtual Physiological Rat project demonstrates that existing tools can create emergent behaviors in multiscale models.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Physiological Modeling

Background:

  • Complex diseases arise from multifactorial influences across various physiological scales.
  • Integrating models from genes to tissues is essential for mechanistic disease description.
  • The Virtual Physiological Rat (VPR) Project aims to mechanistically describe complex diseases using multiscale models.

Purpose of the Study:

  • To identify methods for facilitating model integration across physiological scales.
  • To integrate experimental data into composite multiscale models.
  • To present the state of the art in model integration, sharing, and data archiving.

Main Methods:

  • Utilized existing ontological, model, and data repository resources.
  • Employed integrative software tools for combining separate computational models.

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  • Developed an example composite multiscale model.

    • Main Results:

    • Current resources and tools are sufficient for creating composite models from existing ones.
    • The developed example composite model exhibited emergent behavior not predictable from individual models.
    • Demonstrated the feasibility of integrating experimental data into multiscale models.

    Conclusions:

    • Composite multiscale models can be successfully constructed using current technologies.
    • Integrative modeling approaches reveal emergent properties crucial for understanding complex diseases.
    • The VPR project provides a framework for advancing systems biology research and data sharing.