Single-cell resolved cell-cell communication modeling in Julia

07/29/2021, 12:50 PM1:00 PM UTC
Purple

Abstract:

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.

Description:

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.

Platinum sponsors

Julia Computing

Gold sponsors

Relational AI

Silver sponsors

Invenia LabsConningPumas AIQuEra Computing Inc.King Abdullah University of Science and TechnologyDataChef.coJeffrey Sarnoff

Media partners

Packt Publication

Fiscal Sponsor

NumFOCUS