Single-cell resolved cell-cell communication modeling in Julia
2021-07-29, 12:50–13:00 (UTC), Purple

We develop multiscale models that couple cell-cell communication with cell-internal gene regulatory network dynamics to study cell fate decision-making from a dynamical systems perspective. In JuliaLang, we model cell-cell communication as a Poisson process, and cell-internal dynamics with nonlinear ordinary differential equations, taking advantage of the power of DifferentialEquations.jl. We show that subtle changes in cell-cell communication lead to dramatic changes in cell fate distributions.


The role of cell-cell communication in cell fate decision-making has not been well-characterized through a dynamical systems perspective. To do so, here we develop multiscale models that couple cell-cell communication with cell-internal gene regulatory network dynamics. This allows us to study the influence of external signaling on cell fate decision-making at the resolution of single cells. We study the granulocyte-monocyte vs. megakaryocyte-erythrocyte fate decision, dictated by the GATA1-PU.1 network, as an exemplary bistable cell fate system. Using JuliaLang, we model the cell-internal dynamics with nonlinear ordinary differential equations and the cell-cell communication via a Poisson process.

In this work, through analysis of a wide range of cell-cell communication topologies, we discovered that general principles emerged describing how cell-cell communication regulates cell fate decision-making. We studied a wide range of cell communication topologies through simulation using tools from DifferentialEquations.jl. We also used our high-performance computing cluster to run thousands of simulations in order to understand the limiting behaviors of our model. We show that, for a wide range of cell communication topologies, subtle changes in signaling can lead to dramatic changes in cell fate. We find that cell-cell coupling can explain how populations of heterogeneous cell types can arise. Analysis of intrinsic and extrinsic cell-cell communication noise demonstrates that noise alone can alter the cell fate decision-making boundaries. These results illustrate how external signals alter transcriptional dynamics, provide insight into cell fate decision-making, and provide a framework for modeling cell-cell communication that we expect will be of wide interest to the systems biology community.