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Related Experiment Videos

Physiologic stability and physiologic state

T G Buchman1

  • 1Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA. buchman@wudos2.wustl.edu

The Journal of Trauma
|October 1, 1996
PubMed
Summary
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Physiologic stability in diseases like inflammation suggests multiple stable states, not explained by linear models. Nonlinear interactions may underlie disease resilience, challenging organ-specific therapies.

Area of Science:

  • Physiology
  • Nonlinear Dynamics
  • Systems Biology

Background:

  • Classical physiologic control models are typically linear and do not predict multiple stable states.
  • Some disease states, such as disseminated inflammation, exhibit remarkable stability and resistance to therapy.
  • This stability suggests underlying mechanisms beyond simple linear feedback loops.

Purpose of the Study:

  • To explore the concept of multiple stable physiologic states, including disease states.
  • To investigate how nonlinear dynamics can explain the stability and resistance of certain diseases.
  • To re-evaluate the therapeutic strategies for diseases characterized by physiologic stability.

Main Methods:

  • Utilized a simple nonlinear model to predict the existence of multiple stable states.

Related Experiment Videos

  • Analyzed the role of interconnections among system elements in conferring resistance to perturbation.
  • Considered implications for understanding disease states like disseminated inflammation.

    • Main Results:

    • Nonlinear models successfully predict the existence of multiple stable physiologic states, contrasting with linear models.
    • Interconnections within a nonlinear system enhance its resistance to external disturbances.
    • The stability of certain disease states can be attributed to these nonlinear interactions.

    Conclusions:

    • The existence of stable disease states is consistent with nonlinear systems theory.
    • Therapeutic approaches targeting specific organs may be insufficient if disease stability arises from complex, nonlinear interactions.
    • A systems-level, nonlinear perspective is crucial for understanding and treating resilient diseases.