Multicellular growth as a dynamic network of cells.

Multicellular organisms arise from coordinated interactions between individual cells. In budding yeast, clusters form when daughter cells remain attached to their mothers. Two key features of these clusters are their size and cellular composition. Prior research has shown that clusters grow through cell division and break apart when connections weaken. However, the precise relationship between cellular features and cluster phenotypes remains unclear. This gap motivated researchers to explore how specific parameters influence cluster size and composition. Existing models focus on individual cell behaviors but lack a framework for predicting cluster-level outcomes. The challenge lies in integrating division rates, connection stability, and growth differences into a unified model. Experimental data show that clusters vary in size and composition, but the mechanisms behind these variations are not well understood. Understanding these mechanisms could clarify how simple multicellular structures evolve into more complex forms.

This study aims to determine how cellular features quantitatively influence cluster size and composition in budding yeast. The researchers seek to model cluster growth and breakage using three parameters: division rate, connection break rate, and kissing number. The motivation stems from the need to understand how basic cellular behaviors lead to observable cluster phenotypes. By varying these parameters, the researchers can test their effects on cluster dynamics. The study also examines how different cell types—germ and somatic—affect cluster composition. The goal is to identify which parameters most strongly influence cluster size and cellular diversity. This approach allows for a systematic exploration of multicellular organization. The findings may provide insights into the evolution of multicellular development and organization.

The researchers constructed a computational model where cells are nodes and connections are edges. They varied three parameters: division rate, connection break rate, and kissing number. The model simulates cluster growth and breakage over time. By adjusting these parameters, the researchers observed how cluster size and composition change. They tested the model's predictions against experimental data on cluster behavior. The model incorporates a probability-based framework for connection breakage. This allows for exponential decay in connection survival over time. The researchers also extended the model to include two cell types with different growth rates. This enabled them to study how cellular diversity affects cluster composition. The model's predictions were validated using statistical correlations between parameters and outcomes.

The kissing number determines the maximum possible cluster size in the model. Below this limit, cluster size depends on the ratio of division rate to connection break rate. The model shows that connection breakage follows an exponential decay pattern with age. This behavior aligns with experimental observations of cluster breakage. When two cell types are introduced, cluster composition becomes more diverse. The fraction of clusters containing both cell types increases with higher kissing numbers and growth rate differences. In a population of clusters, cellular composition variation is inversely correlated with somatic cell fraction (r²=0.87). These results suggest that a few cellular features can control cluster phenotypes. The model successfully recapitulates experimental data on cluster growth and breakage.

The study demonstrates that cluster size and composition depend on the kissing number, division rate, and connection break rate. These parameters set quantitative limits on cluster growth and diversity. The model shows that connection breakage follows an age-dependent exponential pattern. This finding supports the idea that cluster stability is influenced by connection longevity. When two cell types are included, cluster composition becomes more variable. The inverse correlation between composition variation and somatic cell fraction suggests a balancing mechanism. The results suggest that simple cellular features can drive complex multicellular organization. These findings may help explain how early multicellular structures evolved into more complex forms.