Agent-based Model for Developmental Aggregation in Myxococcus xanthus Bacteria

Spatial self-organization is widely studied in active matter physics due to its biological significance. Myxococcus xantus is a rod-shaped soil bacterium that can serve as a simple model system to study the self-organization because under different conditions, its cells self-organize into distinct dynamical patterns. To understand how M. xantus cells aggregate into multicellular mounds under starvation conditions, we built an agent-based model. In this model, each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic patterns observed by experiments. However, at high agent density, this model results in formation of cell streams but not stable aggregates. To overcome this problem, we extend the model based on the recent experimental observation that cells tend to have a longer run period when moving towards aggregate. We assumed that cells produce a chemical signal that affects their reversal frequency. Using a phenomenological chemotaxis model with adaptation, we can match run duration bias with observed experimental results. Incorporating these effects in our model leads to the formation of stable aggregates.