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Dynamic generation of physiological model systems


Mathematical models are widely used to simulate physiological processes in the human body and can be exploited for diagnostic purpose or the automation of therapeutic measures [1]. Usually these models focus on one single aspect of the human physiology. Complex models with interaction between different physiological processes usually do not consist of interchangeable submodels. We therefore designed a versatile software based on Matlab with dynamically exchangeable subsystems within the three model families of respiratory mechanics, gas exchange and cardiovascular dynamics.


For each submodel the parameters have been extracted from the corresponding literature. Common interfaces were defined for each model family based on these parameters to ensure interchangeability within the same model family. For simulation of human body gas exchange we used a two-compartment model with oxygen and carbon dioxide dissociation curves. The model family of cardiovascular dynamics consisted of a single-compartment model and a six-compartment model including the response to pleural pressure. The respiratory mechanics were based on a first-order resistance-compliance model and a second-order resistance-compliance model. Model parameters were fit to human test data or to data taken from the literature. Simulation is executed using a dedicated caller program to combine the selected submodels and to execute them subsequently at each time step. Submodel selection and basic parameter specification can be done via a graphical user interface. The software was tested with different model combinations. Each combination was supplied with alterations in ventilation frequency and positive end-expiratory pressure.


Simulation based on parameters from the literature with the variations described above showed plausible results. Alterations in ventilation frequency indicated a response time consistent with data acquired by Jensen and colleagues [2].


The developed software is able to simulate different combinations of submodels at variable complexity. Simulation results are consistent with experimental data. Interaction between submodels can be seen in the simulation output.


  1. 1.

    Lozano S, Moller K, Brendle A, et al.: AUTOPILOT-BT: a system for knowledge and model based mechanical ventilation. Technol Health Care 2008, 16: 1-11.

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  2. 2.

    Jensen MC, Lozano S, Gottlieb D, et al.: An evaluation of end-tidal CO 2 change following alteration in ventilation frequency [abstract]. MBEC Antwerpen Conference 2008.

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Kretschmer, J., Wahl, A., Guttmann, J. et al. Dynamic generation of physiological model systems. Crit Care 13, P151 (2009).

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  • Respiratory Mechanic
  • Model Family
  • Physiological Model
  • Pleural Pressure
  • Ventilation Frequency