Kinetics of porosity and stability of dough expansion during proofing have been fitted with Gompertz and exponential models, respectively, for 24 distinct mixing conditions and same dough composition. Data for 10 conditions were used to relate the parameters of the models to mixing variables, specific power, and texturing time, through power regression models. Interpretation of the relationships between the mixing variables and the parameters of the Gompertz and exponential models emphasises the influence of dough rheological properties on dough expansion during fermentation and likely on bubbles distribution. The prediction performances of these porosity and stability models were evaluated using the root mean square error and mean absolute percentage error, for time series of the remaining 14 mixing conditions. The results show that integrating the mixing variables into the models significantly improves the prediction accuracy compared to control models whose parameters values are arithmetic means. Finally, we present an application where the mixing variables are determined in order to obtain a dough exhibiting the desired features during proofing, such as high levels of porosity and stability. Intensive mixing yields the best result but a more interesting trade-off can be obtained with intermediary mixing processes.