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Related Experiment Video

Updated: Feb 13, 2026

Single-cell Microinjection for Cell Communication Analysis

Single-cell Microinjection for Cell Communication Analysis

Published on: February 26, 2017

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions.

Kevin Thurley¹, Lani F Wu¹, Steven J Altschuler¹

¹Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158, USA.

|March 12, 2018

Summary

This study models cell-to-cell communication networks, treating complex intracellular networks as black boxes. The research identifies principles for controlling population responses and explains cytokine secretion timing in T cells.

Keywords:

cell-to-cell communication cytokine signals first-passage time response-time modeling

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Area of Science:

Systems Biology
Cellular Communication
Computational Biology

Background:

Cell-to-cell communication is vital for organismal processes like tissue development and immune responses.
Understanding cell-to-cell communication networks is less advanced than intracellular signaling pathways.
Challenges include complex intracellular regulation, cell heterogeneity, and feedback loops in communication.

Purpose of the Study:

To develop a framework for understanding and engineering cell-to-cell communication circuits.
To identify conditions generating specific population responses, like bimodality.
To explain puzzling experimental data on T cell cytokine secretion.

Main Methods:

Modeling cell-to-cell communication circuits as interconnected "black box" systems.
Characterizing intracellular networks by their input-output relationships.
Analyzing simple circuit motifs to understand emergent population behaviors.

Main Results:

Identified conditions for generating bimodal temporal responses in cell populations.
Discovered mechanisms for independent control of population synchronization and response delay.
Successfully applied the model to explain cytokine secretion onset times in T cells.

Conclusions:

The "black box" modeling approach simplifies the study of complex communication networks.
This framework allows prediction of communication network structures from input-output data.
The findings offer insights into coordinating cellular behavior and designing synthetic biological systems.