The parameter values in the above equations were taken or estimated from literature in the following discussion. For those whose experimental data are not available, we performed a global sensitivity analysis to obtain order-of-magnitude estimates. Sensitivity analysis is useful to determine parameters that play a critical role in affecting the model outcome . In this study, a global sensitivity analysis outlined by Marino et al.  is applied to assess the sensitivity of the model outcome, TD, at t = 4000 day in the stable stage of COPD to variations of all parameters in the model. Baseline values of these parameters are listed in Table A in S1 File. A range of 10% to 200% of the baseline values was specified for each parameter in a way similar to that in ref. 49. All parameters are assumed to be uniformly distributed in their corresponding interval and 2000 samples are generated for each parameter using the Latin Hypercube Sampling method . The partial rank correlation coefficient (PRCC) as well as p-value for each parameter is then calculated. The calculated PPRCs with the p-values smaller than 0.01 are presented in Figure A in S1 File. The values of PRCC range between -1 and +1 with the sign determining whether an increase in the parameter will increase (+) or decrease (-) the TD output.