Identifying a developmental transition in honey bees using gene expression data

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.Author summary: A complicated biochemical choreography is responsible for both the behavior of cells that make up organisms and, at a larger scale, the behavior of cooperating groups like honey bee colonies. Individuals in these systems often take on specialized roles: cells assume specific types and bees perform specific tasks. Distinct roles are thought to emerge when differences among individuals are amplified. This amplification can create a transition from continuous variation to a case with two separate types. Identifying how and when these transitions occur has been challenging. Here, we create a method inspired by statistical physics to detect this type of collective transition using gene expression data. We demonstrate it by locating a known transition in honey bees between those that stay in the nest and those that leave to find food. We also show that the method can handle the type of large datasets that are now routinely generated in many areas of systems biology.