Stochastic bounds of aggregation dynamics distinguish near-wild-type from wild-type strains in social bacteria

Merrill E. Asp, Eduardo Caro, Roy D. Welch, Alison E. Patteson

Living systems constantly navigate stochasticity to survive. This is especially true in the case of multicellular development, where the interwoven networks of signal transduction, gene expression, and physical response produce emergent phenotypes from the collective behavior of many cells. The genotype-phenotype problem for multicellular development asks how genetic inputs control these collective phenotypes. To study this problem, we use the model organism Myxococcus xanthus, a bacterial species that exhibits a rudimentary but robust form of development in the form of fruiting body aggregation, wherein cells in a low-nutrient environment spontaneously organize from a homogeneous, flat swarm into a discrete number of mound-shaped aggregates harboring thousands of cells.

We present a high-throughput imaging setup and analysis pipeline to produce a dataset of these aggregation events that is sufficiently large to use data-driven statistics to quantify the bounds of wild-type behavior. This approach catches subtle phenotypic distinctions between wild-type and mutant strains of M. xanthus, and can be used as a platform for gene annotation and mechanobiological studies into the genotype-phenotype problem.