Abstract
Collective behavior in cellular populations is coordinated by biochemical signaling networks within individual cells. Connecting the dynamics of these intracellular networks to the population phenomena they control poses a considerable challenge because of network complexity and our limited knowledge of kinetic parameters. However, from physical systems, we know that behavioral changes in the individual constituents of a collectively behaving system occur in a limited number of well-defined classes, and these can be described using simple models. Here, we apply such an approach to the emergence of collective oscillations in cellular populations of the social amoeba Dictyostelium discoideum. Through direct tests of our model with quantitative in vivo measurements of single-cell and population signaling dynamics, we show how a simple model can effectively describe a complex molecular signaling network at multiple size and temporal scales. The model predicts novel noise-driven single-cell and population-level signaling phenomena that we then experimentally observe. Our results suggest that like physical systems, collective behavior in biology may be universal and described using simple mathematical models. Synopsis A simple two-variable model in combination with quantitative in vivo measurements of single-cell and population signaling dynamics is used to analyze the emergence of collective cAMP oscillations in Dictyostelium discoideum. Single Dictyostelium cells are well described as excitable, oscillatory systems. A universal, top-down model reproduces single-cell and population-level behaviors. Model-based predictions are validated in individual cells and in cellular populations. Stochasticity drives the emergence and continued coordination of collective behavior. A simple two-variable model in combination with quantitative in vivo measurements of single-cell and population signaling dynamics is used to analyze the emergence of collective cAMP oscillations in Dictyostelium discoideum.
Original language | English (US) |
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Article number | 779 |
Journal | Molecular Systems Biology |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2015 |
All Science Journal Classification (ASJC) codes
- Information Systems
- General Immunology and Microbiology
- Applied Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Agricultural and Biological Sciences
- Computational Theory and Mathematics
Keywords
- FRET
- dynamical systems
- live microscopy
- phenomenological modeling