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Improving the physiological realism of experimental models.
Kalyan C Vinnakota1, Chae Y Cha2, Patrik Rorsman2
1Department of Molecular and Integrative Physiology , University of Michigan , Ann Arbor, MI , USA.
Interface Focus
|April 7, 2016
Summary
The Virtual Physiological Human (VPH) project needs realistic experimental models (E-Models) to advance computational models (C-Models) for disease research and drug screening. Progress in E-models is crucial for VPH success.
Area of Science:
- Computational biology
- Physiological modeling
- In silico experimentation
Background:
- The Virtual Physiological Human (VPH) project aims to create advanced computational models (C-Models) for disease study and drug development.
- VPH success hinges on integrating experimental data and utilizing physiologically realistic experimental models (E-Models).
- Current E-models range from in vitro cell organelles to in vivo human organ systems.
Purpose of the Study:
- To discuss the current state of experimental models (E-Models) relevant to the Virtual Physiological Human (VPH) project.
- To highlight challenges and recent advancements in enhancing the physiological realism of E-Models.
- To explore how improved E-Models can benefit VPH initiatives in disease research and therapeutic development.
Main Methods:
- Review of existing literature on experimental models (E-Models) across various biological scales.
- Analysis of physiological realism in E-Models for applications in computational modeling.
- Case studies from cardiovascular disease, musculoskeletal disorders, diabetes, and Parkinson's disease research.
Main Results:
- Identified a spectrum of E-Models from cellular to whole-organ systems.
- Highlighted key challenges in achieving and validating physiological realism in E-Models.
- Showcased progress in developing more sophisticated E-Models applicable to VPH.
Conclusions:
- Advancements in physiological realism of E-Models are critical for the success of the Virtual Physiological Human (VPH) project.
- Improved E-Models will enhance the accuracy and predictive power of computational models (C-Models).
- Continued research in E-Models is essential for in silico drug screening and personalized medicine.
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