Individual-based models (IBMs), the biological agent-based models, are currently being applied to the study of microbial systems. A microbial IBM of yeast populations growing in liquid bath cultures has already been designed and implemented in the simulator called INDISIM-YEAST. In order to improve its predictive capabilities and further its development, a deeper understanding of how the variation of the output of the model can be apportioned, qualitatively or quantitatively, to different sources of variation must be investigated. The aim of this study is to show how insights into the individual cell parameters of INDISIM-YEAST can be obtained combining local and global methods using classic and well-proven methods, and to illustrate how these simple methods provide useful, reliable results with this IBM. This work deals mainly with the use of screening methods, as the main task to perform here is that of identifying the most influential factors for this microbial IBM. This screening exercise has allowed the establishment of significant input factors to this IBM on yeast population growth, and the highlighting of those that require greater attention in the parameterization and calibration processes.