# Mathematical modeling of variability in intracellular signaling

## TL;DR

This paper reviews recent advances in mathematical modeling of cellular signaling variability, emphasizing mechanistic models and their calibration to understand cell-to-cell heterogeneity.

## Contribution

It provides a comprehensive overview of modeling approaches for intracellular signaling variability, highlighting recent developments and future challenges.

## Key findings

- Mechanistic models help interpret single-cell signaling data.
- Recent advances improve model calibration and predictive power.
- Challenges include data integration and model validation.

## Abstract

Cellular signaling is essential in information processing and decision making. Therefore, a variety of experimental approaches have been developed to study signaling on bulk and single-cell level. Single-cell measurements of signaling molecules demonstrated a substantial cell-to-cell variability, raising questions about its causes and mechanisms and about how cell populations cope with or exploit cellular heterogeneity. To gain insights from single-cell signaling data, analysis and modeling approaches have been introduced. This review discusses these modeling approaches, with a focus on recent advances in the development and calibration of mechanistic models. Additionally, it outlines current and future challenges.