Multiscale modeling of oscillations and spiral waves in Dictyostelium populations

TL;DR

This paper develops multiscale models to explain how spiral waves and oscillations emerge in Dictyostelium populations, linking biochemical networks within cells to collective spatial behaviors.

Contribution

The authors introduce new multiscale models that incorporate recent experimental data, capturing complex collective behaviors like spiral wave formation in Dictyostelium populations.

Findings

Models reproduce spiral wave phenomena in simulations.

Identification of density-dependent oscillation frequency.

Discovery of bistability and dynamic death in the system.

Abstract

Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales -- from biochemical networks within individual cells to spatially structured cellular populations. Here, we present a family of multiscale models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cAMP used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various pre-processing modules. We use this family of models to study spatially unstructured populations by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then extend our models to include spatial structure and show how they naturally give rise to spiral waves. Our models exhibit a wide range of novel phenomena including a density dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.