## 3 Discussion
However, their results focused on global shape and size of colonies instead of spatial organization of cells, and they did not consider colonies larger than several hundred cells. [PERSON] et al. developed a center-based model to study the impact of budding patterns, mating type switches, cell death and nutrient limitation on yeast colony growth [42]. Consistent with [PERSON] et al., they found that bipolar budding patterns improve colony development under nutrient limitation. In addition, they found that axial budding patterns enhance mating probability during early stages of colony growth and suggest that the frequency of mating type switch might control the trade-off between diploidization and inbreeding. Interestingly, they showed that colony expansion does not depend on the overall age of the colony. They hypothesize this is due to the fact that young cells contribute most to colony expansion which is consistent with our results. One important difference between our model and the model presented by [PERSON] et al., is that our model explicitly includes the process of a bud growing on the mother cell. The model by [PERSON] et al. skips the growing process and introduces new daughter cells after they have detached from the mother cell and are a more substantial size. In addition, their study focused on characterizing heterogeneity of cell types in the colony and overall shape and size of the colony and they did not analyze the impact of spatial organization of cells. Since their model does incorporate many of the important factors we discussed above as possibly having an impact on spatial organization and structure of the colony, we believe an interesting avenue for future work would be to extend our model to include the same factors they considered and investigate how the addition of the budding process changes the resulting outcomes discussed in their paper. Many questions remain in investigating the structure of growing yeast colonies, and our model may be easily generalized to include them. In particular, we plan to extend our model by including intracellular protein dynamics to more directly study prion sectors in yeast. Despite many years of biomedical research and our detailed understanding of protein aggregation dynamics on the molecular scale, our ability to mechanistically link protein aggregation processes to their disease phenotypes at the colony level is severely lacking, especially during transitions between prion states [62; 63; 64; 65]. For example, recent studies have demonstrated that a single colony can exhibit multiple phenotypes resulting from a change in aggregation dynamics between neighboring cells [3]. Our results in Section 2.2.4 demonstrate that nutrient limitation and budding together have a large impact on emergent patterns of sector-like regions in growing yeast colonies. Moreover, an important insight of our work is the large variation in subcolony sizes that arises due to nutrient-limited growth. As a consequence of this result, we can better understand that the size of a subcolony sector is not directly correlated to its birth order. Thus, quantitative information connecting spatial information of sectoring patterns with molecular level dynamics is required. What is missing is a detailed and mechanistic computational model of sectoring phenomena which can then be coupled with an informatics look at the sectors in experiments. Methods and tools for counting different colonies have been developed which may aid in detecting phenotype sectors through finding connected groups of cells [66; 67; 68]. While there are resources that quantify sectoring in microbial colonies [5; 8], to our knowledge no other studies specifically quantify the causes of phenotype sectoring in yeast colonies. Our modeling framework can be readily extended to a multi-scale system to address phenotype sectoring in yeast through the incorporation of prion dynamics as a subcellular process (Figure 1).