A Mathematical Approach for Gas Exchange in the Lungs; Balance Between Ventilation and Perfusion

Abstract

IntroductionChronic pulmonary diseases and respiratory infections remain as one of the most morbid and deadliest diseases in the world. Airway and environment play an important role in the etiology of these pathologies. The balance between oxygen and carbon dioxide exchange is the pillar of the respiratory system. The aim of this project is to understand the gas exchange in the lungs using a mathematical model that describes the dynamics in each alveoli, in order to evaluate the relationship between ventilation and perfusion.MethodsA mathematical model was design based on the alveoli gas exchange and its relationship between ventilation and perfusion (V/Q). So, the lungs were divided into 9 segments, each one with different ratio according to their relative exchange area and the anatomical and physiological characteristics. The model assumed mass balance for O2 and CO2 and ideal gas law. Parameters for the model were determined according to data obtained in the literature. The model was implemented in MATLAB (The MathWorks®, Inc.) and the equations were solved to steady state.ResultsThe model predicted gas concentrations and flows in different lung segments. There was an evident strong association between V/Q, and its mechanisms to compensate. The model showed the behavior of lungs with and without disease. One of the most important outcomes was the evidence of enviromental influence (i.e. altitude, polution) in the imparment of the small airways and its association with the cardiovascular system.ConclusionThe mathematical model was helpful to understand the gas exchange dynamics. The model can be used to predict the interaction between ventilation and perfusion under different conditions, giving the possibility to estimate the efficacy of medical intervention. It can also shed some light on how to prevent and decrease the incidence of lung diseases.Support or Funding InformationResearch Group in Mathematical and Computational Biology (BIOMAC), Department of Biomedical Engineering, Universidad de los Andes, Colombia.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.