Open AccessA self-exciting point process to study multicellular spatial signaling patterns
Author(s) -
Archit Verma,
Siddhartha G. Jena,
Danielle R. Isakov,
Kazuhiro Aoki,
Jared E. Toettcher,
Barbara E. Engelhardt
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2026123118
Subject(s) - multicellular organism , signal transduction , cell signaling , signaling proteins , process (computing) , computer science , point process , biology , computational biology , microbiology and biotechnology , cell , mathematics , genetics , operating system , statistics
Significance Cells are under constant pressure to integrate information from both their environment and internal cellular processes. However, these effects often use the same signaling pathways, making autonomous and coupled signaling difficult to decouple from one another. Here, we present a statistical modeling framework, the cellular point process (CPP), that decouples these two modes of signaling using videos of living, actively signaling cells as input. Our model reveals modulation of autonomous and coupled signaling parameters in a number of contexts ranging from pharmacological treatment to wound healing that were previously unavailable. The CPP enhances our understanding of cellular information processing and can be extended to a wide range of systems.