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Related Experiment Videos

Developing and testing integrated multicompartment models to describe a single-input multiple-output study using the

D M Foster1

  • 1Department of Bioengineering 352255, University of Washington, Seattle 98195-2255, USA.

Advances in Experimental Medicine and Biology
|October 22, 1998
PubMed
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This study demonstrates developing and testing a model for complex input-output studies using SAAM II software. The methods allow interpretation of data from multiple sites, aiding in understanding system dynamics not directly measured.

Area of Science:

  • Pharmacokinetics and Systems Biology
  • Mathematical Modeling
  • Software Development

Background:

  • Sophisticated measurement devices enable complex input-output studies with data from multiple system sites.
  • Interpreting such data requires models that integrate existing knowledge and describe observed data.
  • Radiolabeled substance studies in plasma, red cells, and urine are common in physiological research.

Purpose of the Study:

  • To illustrate the development and testing of a model structure for single-input multiple-output studies.
  • To demonstrate the use of SAAM II software for creating and validating complex biological models.
  • To show how to model systems with both measurable and unmeasurable components.

Main Methods:

  • Utilizing SAAM II software for developing and testing model structures.

Related Experiment Videos

  • Employing forcing function capability to decouple system components.
  • Postulating and testing model structures for subsystems and the overall system.

    • Main Results:

    • Successful development of a model structure for a single-input multiple-output study.
    • Demonstration of how to integrate data from various measurement sites.
    • Ability to infer properties of unmeasured system parts.

    Conclusions:

    • SAAM II facilitates the creation of sound models for complex physiological studies.
    • The described modeling approach allows for comprehensive system analysis.
    • Model testing provides insights into system behavior and goodness-of-fit.