Data-driven quantification of robustness and sensitivity of cell signaling networks

TL;DR

This paper introduces a data-driven MaxEnt method to quantify robustness and sensitivity in cell signaling networks, accounting for cell-to-cell variations and predicting single-cell behaviors from population data.

Contribution

It presents a novel MaxEnt-based approach that incorporates experimental cell-to-cell variability to assess robustness and predict single-cell responses in signaling networks.

Findings

Correctly ranks E. coli chemotaxis models by robustness

Suggests evolutionary selection for parameter variability

Accurately predicts single-cell responses from population data

Abstract

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data driven Maximum Entropy (MaxEnt) based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.