A dynamic network model predicts the phenotypes of multicellular clusters from cellular properties.

Area of Science:

• Cellular biology
• Developmental biology
• Mathematical modeling

Background:

• Cell division without separation in budding yeast forms multicellular clusters.
• Cluster characteristics include size and cellular composition (phenotype fractions).

Purpose of the Study:

• To model yeast cluster growth and breakage using mathematical parameters.
• To investigate factors influencing cluster size, composition, and the development of synthetic differentiating clusters.

Main Methods:

• Developed a model representing cells as nodes and connections as edges.
• Varied cell division rate, connection breaking rate, and kissing number.
• Modeled link survival probability decreasing exponentially with age.
• Examined synthetic clusters with germ and somatic cells.

Main Results:

• Kissing number determines maximum cluster size; division/breaking rate ratio controls size below this limit.
• Link survival probability decreases exponentially with age, matching experimental data.
• Fraction of mixed-type clusters increases with kissing number and growth rate differences.
• Variation in cellular composition inversely correlates with average somatic cell fraction (r² = 0.87).

Conclusions:

• A few cellular features significantly control multicellular cluster phenotypes.
• These findings provide insights into the evolution of multicellularity and development.