Detection of signaling mechanisms from cellular responses to multiple cues

TL;DR

This paper introduces a systematic inverse modeling approach to deduce minimal signaling networks from cellular responses to multiple cues, especially when responses are antagonistic, aiding understanding of complex cell signaling mechanisms.

Contribution

It presents a novel method to infer minimal, interpretable signaling mechanisms from cell-level data, focusing on antagonistic responses to multiple cues.

Findings

Identified minimal signaling mechanisms explaining antagonistic responses.

Developed a systematic approach for deducing molecular networks from experimental data.

Demonstrated the method's effectiveness in simplifying complex signaling networks.

Abstract

Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to {deduce} the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to {deduce} molecular mechanisms from cell-level data.