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Ventilatory control in hypercapnia and exercise: optimization hypothesis.

C S Poon

    Journal of Applied Physiology (Bethesda, Md. : 1985)
    |June 1, 1987
    PubMed
    Summary
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    This study proposes a respiratory control model where the brain balances gas exchange needs with breathing effort. This model accurately predicts how ventilation changes during CO2 inhalation and exercise.

    Area of Science:

    • Respiratory physiology
    • Computational modeling
    • Systems biology

    Background:

    • The respiratory system's control is complex, involving chemical stimuli and mechanical factors.
    • Understanding how the body regulates breathing during challenges like CO2 inhalation and exercise is crucial.

    Purpose of the Study:

    • To develop and validate a model of the respiratory control system.
    • To investigate the interplay between chemical feedback and respiratory mechanical discomfort in setting ventilatory output.
    • To explore if a unified model can explain responses to hypercapnia, exercise, and ventilatory loading.

    Main Methods:

    • A mathematical model of respiratory control was developed, incorporating chemical and neuromechanical feedback.
    • The model postulates that ventilatory output (VE) minimizes a net operating cost function.

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  • The model includes constraints of arterial PCO2, respiratory mechanical discomfort, and maximum ventilatory capacity (Vmax).

    • Main Results:

    • The model accurately predicted steady-state ventilatory responses to CO2 inhalation and exercise.
    • Model simulations closely mimicked observed ventilatory responses during hypercapnia, exercise, and ventilatory loading.
    • Quantitative validation showed good fits to experimental data, with predicted Vmax averaging 77% of maximum voluntary ventilation.

    Conclusions:

    • The proposed optimization mechanism for respiratory control is plausible.
    • Respiratory mechanical factors play a significant role in regulating ventilatory output (VE).
    • A single model framework can explain ventilatory control across various physiological challenges without exercise-specific signals.