A self-exciting point process to study multicellular spatial signaling patterns

Abstract

Multicellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multicellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes—self-exciting point processes—that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP), we capture both the single-cell pathway activation rate and the magnitude and duration of signaling between cells relative to their spatial location. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes signaling changes across a wound front, and generalizes to multichannel observations.

Learning the rules of collective cell migration using deep attention networks

“…Finally, we investigated the impact of modifications to cell signaling on the attention maps. Here, we perturbed the canonical MDCK model cell system with a drug selected to impact epidermal growth factor (EGF)-TAPI-1-which has been shown to inhibit spatial signaling and extracellular signal-regulated kinase (Erk) activation, and thereby collective migration [52,53] . Specifically, EGF disruption nearly abolished the relative importance of immediate forward neighbors, shifting the focus to immediate left and right neighbors.…”

Towards ‘end-to-end’ analysis and understanding of biological timecourse data

“…Key measurements to understand the dynamics of a given pathway include the fraction of time spent in an active state, the time that passes between periods of pathway activation in a single cell, and evidence of spatial coupling in pathway activity. With the proper reporters and sufficient cell numbers (usually hundreds of imaged cells), all of these parameters can be recovered using timecourse analysis as well as more sophisticated spatial statistics [ 12 , 47 ].…”

Hawkes Process Modeling of Block Arrivals in Bitcoin Blockchain

“…Point processes are probabilistic models of events in a mathematical space that are commonly used to represent event occurrences over time or space (or both) [20]. The conditional intensity function λ(•), which expresses the expected infinitesimal rate at which events occur, can be used to define a point process [21].…”